Serena Janho Dit Hreich

Jonathan Benzaquen

Paul Hofman

Valérie Vouret- Craviari

Dr Valérie

To inhibit or to boost the ATP/P2RX7 pathway to fight cancer, that is the question

Keywords: purinergic signalling, P2X7, immunotherapies, tumors, antitumor immunity, ectonucleases

Despite new biological insights and recent therapeutic advances, many tumors remain at baseline during treatments. Therefore, there is an urgent need to find new therapeutic strategies to improve the care of patients with solid tumors. P2RX7 receptor (P2XR7), an ATPgated ion channel characterized by its ability to form large pore within the cell membrane, is described by most of the investigators as a "chef d'orchestre" of the antitumor immune response. The purpose of this review is to detail the recent information concerning different cellular mechanisms linking P2RX7 to hallmarks of cancer and to discuss different progresses in elucidating how activation of the ATP/P2RX7/NLRP3/IL-18 pathway is a very promising approach to fight cancer progression by increasing antitumor immune responses.

Introduction

According to the International Agency for Research on Cancer (IARC), the global burden of cancer reached 19.3 million new cases and 9.6 million deaths in 2020 (gco.iarc.fr). One in five men and one in six women worldwide will develop cancer in their lifetime, and one in eight men and one in 11 women will die from the disease (gco.iarc.fr). Nowadays, the five-year prevalence, corresponding to the total number of people living with cancer within five years of diagnosis, is estimated at 50.55 million.

At the beginning of cancer therapy, tumors were surgically removed, then came the time of chemotherapies -with molecules killing dividing cells -in association or not with radiation therapies. During the last 25 years, new approaches revolutionized clinical cancer care in daily practice. The first one relies on the understanding of oncogene dependent cell autonomous proliferation, leading to clinically relevant targeted therapies based on molecular targets. The second one relies on immune oncology and led to the development of immunotherapeutic strategies designed to turn on/off specific immune checkpoints to promote antitumor immunity. Other orientations for fighting cancer progression are continuously on progress and may open new doors for some therapeutic opportunities [START_REF] Hofman | New insights into the interaction of the immune system with nonsmall cell lung carcinomas[END_REF]. However, despite this new arsenal of therapeutics many patients still do not recover and/or relapse after a variable period of time, therefore testifying for the need of new drug development to fight cancer. In this review, we focus our attention on the P2X7 purinoreceptor (P2RX7), the only receptor being activated by high levels of extracellular adenosine triphosphate (eATP) and discuss recent progress in elucidating how activation of the ATP/P2RX7/NLRP3/IL-18 pathway can be a promising approach to fight cancer diseases by increasing antitumor immunity.

Exploring the tumor microenvironment to find new therapeutic strategies

It is now well documented that vascular networks and supporting cells are required to ensure the expansion of healthy tissues as well as the growth of tumors [START_REF] Farc | An overview of the tumor microenvironment, from cells to complex networks (Review)[END_REF]. Thus, in addition to directly target tumor cells, the non-cancerous cells present in the tumor microenvironment (TME) can also be viewed as relevant targets, even if this domain is now more and more complex [START_REF] Bagaev | Conserved pan-cancer microenvironment subtypes predict response to immunotherapy[END_REF]. These cells are mesenchymal supporting cells (epithelial, myoepithelial, fibroblasts, adipocytes), cells of the vascular system (endothelial, perivascular cells) and cells of the immune system (lymphoid and myeloid lineages). Altogether, they participate in the development of tumors as nicely illustrated by Egeblad and coauthors [START_REF] Egeblad | Tumors as organs: complex tissues that interface with the entire organism[END_REF]. Further, the continuous interplay between tumor cells and normal surrounding cells influences tumor growth. Indeed, it is well established that fibroblasts, the most abundant population within connective-tissue cells, fuel tumor growth [START_REF] Kuzet | Fibroblast activation in cancer: when seed fertilizes soil[END_REF]. Transforming growth factor beta (TGF-β), a growth hormone secreted by tumor and immune cells, favors the conversion of normal fibroblasts to carcinoma-associated fibroblasts by feeding an autocrine loop involving TGF-β and SDF-1 cytokines, which further lead to the production of tumor promoting chemokines [START_REF] Orimo | Stromal Fibroblasts Present in Invasive Human Breast Carcinomas Promote Tumor Growth and Angiogenesis through Elevated SDF-1/CXCL12 Secretion[END_REF][START_REF] Kojima | Autocrine TGF-and stromal cell-derived factor-1 (SDF-1) signaling drives the evolution of tumor-promoting mammary stromal myofibroblasts[END_REF] and modulate the composition of immune cells within the TME where they can promote or inhibit tumor growth [START_REF] Gajewski | Innate and adaptive immune cells in the tumor microenvironment[END_REF][START_REF] Hanahan | Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment[END_REF][START_REF] Marvel | Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected[END_REF]. Is there a common intermediate between all these cells and if so, what could it be? An important step to answer this question was taken by Geoff Burnstock decades ago when he postulated that ATP is released by most cells and can be considered as an extracellular signaling molecule [START_REF] Burnstock | Evidence that adenosine triphosphate or a related nucleotide is the transmitter substance released by nonadrenergic inhibitory nerves in the gut[END_REF]. This discovery was further sustained by the demonstration that eATP promotes calcium influx in macrophages [START_REF] Steinberg | Extracellular ATP4-promotes cation fluxes in the J774 mouse macrophage cell line[END_REF], via the seventh member of P2X purinoreceptor family, formerly identified as the cytolytic P2Z receptor [START_REF] Surprenant | The cytolytic P2Z receptor for extracellular ATP identified as a P2X receptor (P2X7)[END_REF], which is described below. Finally, the importance of the ATP/P2RX7 pathway in the field of oncology was buried by the discovery that ATP is present at high levels (millimolar order) in tumor site [START_REF] Pellegatti | Increased level of extracellular ATP at tumor sites: In vivo imaging with plasma membrane luciferase[END_REF]. Of interest, all mammalian cells express P2RX7 mRNA and most of the cells express a functional receptor, i.e.

a receptor expressed at the cell membrane, with a channel and a macropore opening activities [START_REF] Benzaquen | Alternative splicing of P2RX7 pre-messenger RNA in health and diseases: Myth or reality?[END_REF]. In particular, functional P2RX7 was described in cells that composed the TME, such as fibroblasts [START_REF] Solini | Human primary fibroblasts in vitro express a purinergic P2X 7 receptor coupled to ion fluxes , microvesicle formation and IL-6 release[END_REF], endothelial cells [START_REF] Oliveira Sd Dos | Endothelial P2X7 receptors' expression is reduced by schistosomiasis[END_REF], dendritic cells [START_REF] Georgiou | Human Epidermal and Monocyte-Derived Langerhans Cells Express Functional P2X 7 Receptors[END_REF][START_REF] Baroni | Stimulation of P2 ( P2X 7 ) receptors in[END_REF][START_REF] Idzko | Nucleotides induce chemotaxis and actin polymerization in immature but not mature human dendritic cells via activation of pertussis toxin-sensitive P2y receptors[END_REF], monocytes/macrophages [START_REF] Gu | of P2X7 purinoceptors on human lymphocytes and monocytes: evidence for nonfunctional P2X7 receptors[END_REF][START_REF] Chiozzi | Spontaneous Cell Fusion in Macrophage Cultures Expressing High Levels of the P2Z/P2X 7 Receptor[END_REF], lymphocytes [START_REF] Borges | The purinergic receptor P2RX7 directs fitness of long-lived memory CD8 + T cells[END_REF], M-and G-MDSC [START_REF] Bianchi | ATP/P2X7 axis modulates myeloidderived suppressor cell functions in neuroblastoma microenvironment[END_REF] and neutrophils [START_REF] Christenson | Serum amyloid A inhibits apoptosis of human neutrophils via a P2X7-sensitive pathway independent of formyl peptide Abstract : Neutrophil apoptosis is important for the termination of inflammatory reactions , in that[END_REF] as illustrated in Figure 1. In addition, expression of a functional P2RX7 was found in both solid [START_REF] Deli | Functional genomics of calcium channels in human melanoma cells[END_REF][START_REF] Solini | Increased P2X 7 Receptor Expression and Function in Thyroid Papillary Cancer : A New Potential Marker of the Disease ?[END_REF][START_REF] Fg | Paracrine Stimulation of P2X7 Receptor by ATP Activates a[END_REF][START_REF] Santos | Potential role of P2X7R in esophageal squamous cell carcinoma proliferation[END_REF] and liquid cancers [START_REF] Adinolfi | Brief report P2X 7 receptor expression in evolutive and indolent forms of chronic B lymphocytic leukemia[END_REF][START_REF] Zhang | Expression of P2X7 in human hematopoietic cell lines and leukemia patients[END_REF][START_REF] Chong | Abnormal expression of P2X family receptors in Chinese pediatric acute leukemias[END_REF].

The P2X7 purinoreceptor: A signaling hub underpinning tumor onset

The P2X7 purinoreceptor (also called P2X7R or P2RX7) belongs to the purinergic receptor family, which is divided into two major families: P2X and P2Y [START_REF] Jacob | Purinergic signaling in inflammatory cells: P2 receptor expression, functional effects, and modulation of inflammatory responses[END_REF]. These receptors have attracted growing interest since the discovery that inflammatory and cancer tissues contain high levels of eATP and adenosine, its immunosuppressive degradation product [START_REF] Pellegatti | Increased level of extracellular ATP at tumor sites: In vivo imaging with plasma membrane luciferase[END_REF][START_REF] Allard | Immunosuppressive activities of adenosine in cancer[END_REF].

Depending on the receptor involved, extracellular purines orchestrate either immune stimulation or immune suppression of host cells, as well as proliferation or cytotoxicity of tumor cells [START_REF] White | P2 receptors and cancer[END_REF].

P2RX7 is an ATP-gated ion channel composed of three protein subunits (encoded by the P2RX7 gene). Each monomer is composed of two transmembrane domains that are linked by a large extracellular domain made of 285 amino acids; The N and C termini are intracellular, with a very long C-terminus, comprising various moieties such as trafficking and lipid interaction domains and death domain, as nicely illustrated in [START_REF] Kopp | P2X7 Interactions and Signaling -Making Head or Tail of It[END_REF]. This particular feature makes P2RX7 unique, as well as the fact that it is activated by high concentrations of eATP, implicating that it remains active where and when the other P2X receptors showed no current upon agonist application [START_REF] North | Pharmacology of Cloned P2X Receptors[END_REF]. Activated P2RX7 stimulates the entry of Ca 2+ and Na + but also the efflux of K + . Intracellular Ca 2+ is an ubiquitous second messenger known to control various cellular functions such as cell homeostasis, gene expression, cell survival/death, exo and endocytosis and contraction [START_REF] Bagur | Intracellular Ca 2+ Sensing: Its Role in Calcium Homeostasis and Signaling[END_REF]. The ATP/P2RX7-induced increase of intracellular Ca 2+ levels are highly relevant in multiple cell responses. In macrophages, P2RX7-dependant calcium entry and subsequent activation of the PKC/c-Src/Pyk2/ERK1/2 pathway have been shown to produce reactive oxygen species (ROS) [START_REF] Martel-Gallegos | Oxidative stress induced by P2X7 receptor stimulation in murine macrophages is mediated by c-Src/Pyk2 and ERK1/2[END_REF], but also lipase activation and blebbing [START_REF] Kopp | P2X7 Interactions and Signaling -Making Head or Tail of It[END_REF]. In the nervous system, P2RX7 activation has been demonstrated to modulate sodium currents and intrinsic neuron excitability [START_REF] Del Puerto | ATP-P2X7 Receptor Modulates Axon Initial Segment Composition and Function in Physiological Conditions and Brain Injury[END_REF]. Ion channel function is independent of the long C-terminus, as its deletion or truncation do not alter it [START_REF] Smart | P2X7 receptor cell surface expression and cytolytic pore formation are regulated by a distal C-terminal region[END_REF]. Accordingly, the truncated P2RX7B isoform retains some ATPinduced channel activity and expression of P2RX7B in HEK293 cells increases stimulates growth [START_REF] Adinolfi | Trophic activity of a naturally occurring truncated isoform of the P2X7 receptor[END_REF]. However, it is has been shown that domains lying outside of the pore also participate in the regulation of Ca 2+ flux [START_REF] Liang | Quantifying Ca2+ current and permeability in ATP-gated P2X7 receptors[END_REF].

In addition to increase intracellular Ca 2+ concentrations, P2RX7 activation leads to a decrease in intracellular K + which has been recognized as one of the main activators of NLRP3 inflammasome [START_REF] Pétrilli | Activation of the NALP3 inflammasome is triggered by low intracellular potassium concentration[END_REF]. Activation of NLRP3 inflammasome is intimately linked to innate immunity being involved in the production of two key potent proinflammatory cytokines, IL-1β and IL-18 which have also been described to be important in tumor onset as they stimulate the production of Interferon gamma (IFN-) by immune effector cells (T lymphocytes, Natural Killer) [START_REF] Ghiringhelli | Activation of the NLRP3 inflammasome in dendritic cells induces IL-1Β-dependent adaptive immunity against tumors[END_REF][START_REF] Ma | ATP-dependent recruitment, survival and differentiation of dendritic cell precursors in the tumor bed after anticancer chemotherapy[END_REF]. In addition, activation of NLRP3 inflammasome leads to the formation of pores in the plasma membrane, via Gasdermin D [START_REF] Pelegrin | P2X7 receptor and the NLRP3 inflammasome: Partners in crime[END_REF], that results ultimately to pyroptotic cell death.

Yet, P2RX7 activation by itself is also associated with the formation of a large pore. After decades of research and various hypothesis (mainly pore dilatation and accessory proteins), it is now well admitted that the pore formation is intrinsic of the channel activity [START_REF] Harkat | On the permeation of large organic cations through the pore of ATP-gated P2X receptors[END_REF][START_REF] Pippel | Localization of the gate and selectivity filter of the full-length P2X7 receptor[END_REF] and can be regulated by the C-terminus which interacts with phosphatidylglycerols and sphingomyelins of the cell membrane to facilitate cell permeability [START_REF] Karasawa | The P2X7 receptor forms a dye-permeable pore independent of its intracellular domain but dependent on membrane lipid composition[END_REF]. Karasawa and co authors also elucidated why the truncated P2RX7 was devoided of pore formation by demonstrating that the C-terminal Cys-rich region, which is deleted in the truncated isoform, counteracts the inhibitory effect of cholesterol, as previously discussed [START_REF] Benzaquen | Alternative splicing of P2RX7 pre-messenger RNA in health and diseases: Myth or reality?[END_REF][START_REF] Virgilio | The Elusive P2X7 Macropore[END_REF].

As already mentioned, the long intracellular C-terminus of P2RX7 is required for most of the signaling pathways induced by the ATP/P2RX7 axis. We will focus here on P2RX7-induced cell responses that are related to hallmarks of cancer (Figure 2) as described by Hanahan et al.

P2RX7 and sustained cell proliferation

Sustained proliferation is a characteristic of tumor cells and is under the control of growth factors that bind receptors with intracellular tyrosine kinase domains which further stimulate intracellular signaling pathways leading to cell proliferation. ATP, the natural ligand of P2RX7 does not belong to the growth factor family and no tyrosine kinase domains have been described in the C-terminus of P2RX7. However, several reports support the notion that P2RX7 activation by low concentration of ATP, as compared to concentrations inducing cell death, leads to cell proliferation [START_REF] Adinolfi | Expression of P2X7 receptor increases in vivo tumor growth[END_REF][START_REF] Haanes | The P2X7 Receptor Supports Both Life and Death in Fibrogenic Pancreatic Stellate Cells[END_REF][START_REF] Giannuzzo | The P2X7 receptor regulates cell survival, migration and invasion of pancreatic ductal adenocarcinoma cells[END_REF][START_REF] Zhang | Highly-expressed P2X7 receptor promotes growth and metastasis of human HOS/MNNG osteosarcoma cells via PI3K/Akt/GSK3β/βcatenin and mTOR/HIF1α/VEGF signaling[END_REF][START_REF] Zhang | PI3K/Akt/GSK-3β signal pathway is involved in P2X7 receptor-induced proliferation and EMT of colorectal cancer cells[END_REF]. Even in the absence of tyrosine kinase domains, stimulation of the ATP/P2RX7 axis can trigger kinase activation. For instance, Cellular Sarcoma Tyrosine Kinase (c-Src), Mitogen Activated Protein Kinases (ERK1/2, p38, JNK), Phosphoinositide 3-Kinase (PI3Ks)/Protein Kinase B (PKB/Akt) and Protein Kinases C (PKC) have been shown to be activated by P2RX7 [START_REF] Kopp | P2X7 Interactions and Signaling -Making Head or Tail of It[END_REF]. However, no proof of direct interactions exists, and it is likely that kinase activation is mediated via Ca 2+ and/or colocalization of P2RX7 and these kinases in lipid rafts. Accordingly, a lipid interaction motif has been described in the C-terminus of P2RX7 (amino acid 574-589) and P2RX7 move toward lipid rafts in response to ATP [START_REF] Denlinger | Cutting Edge: The Nucleotide Receptor P2X 7 Contains Multiple Protein-and Lipid-Interaction Motifs Including a Potential Binding Site for Bacterial Lipopolysaccharide[END_REF][START_REF] Gonnord | Palmitoylation of the P2X7 receptor, an ATP-gated channel, controls its expression and association with lipid rafts[END_REF][START_REF] Robinson | Plasma membrane cholesterol as a regulator of human and rodent P2X7 receptor activation and sensitization[END_REF][START_REF] Murrell-Lagnado | Regulation of P2X Purinergic Receptor Signaling by Cholesterol[END_REF]. In addition, the proteomic characterization of P2RX7 protein complex in HEK293 cells expressing the rat P2RX7 isoform identified 11 proteins, some of them being involved in cytoskeletal rearrangement [START_REF] Kim | Proteomic and functional evidence for a P2X7 receptor signalling complex[END_REF], a cell response directly linked to cell proliferation. However, the effect of P2RX7 on sustained cell proliferation is controversial. For instance, in glioma tissue, inhibition of P2RX7 by ShRNA increased the expression of EGF and phosphor-EGF protein [START_REF] Fang | P2X7R suppression promotes glioma growth through epidermal growth factor receptor signal pathway[END_REF] and it was reported that reduced expression of P2RX7 may be related to the development of several cancers [START_REF] Benzaquen | Alternative splicing of P2RX7 pre-messenger RNA in health and diseases: Myth or reality?[END_REF][START_REF] Feng | Vascular CD39/ENTPD1 Directly Promotes Tumor Cell Growth by Scavenging Extracellular Adenosine Triphosphate[END_REF][START_REF] Feng | A truncated P2X7 receptor variant (P2X7-j) endogenously expressed in cervical cancer cells antagonizes the full-length P2X7 receptor through hetero-oligomerization[END_REF][START_REF] Lara | P2X7 in Cancer: From Molecular Mechanisms to Therapeutics[END_REF].

P2RX7 and growth suppressor evasion

To hijack programs that negatively regulate cell proliferation is as important as promoting cell proliferation. To our knowledge P2RX7 does not inhibit tumor suppressors such as TP53 or retinoblastoma-associated (RB) proteins. On the contrary, it was reported that the ATP/P2RX7 axis supports apoptosis and necrosis by increasing p53 protein levels in mesangial [START_REF] Schulze-Lohoff | Extracellular ATP causes apoptosis and necrosis of cultured mesangial cells via P2Z/P2X 7 receptors[END_REF] and human cervical cancer cells [START_REF] Mello P De | Adenosine uptake is the major effector of extracellular ATP toxicity in human cervical cancer cells[END_REF].Whether the death domain localized in the C-terminus of P2RX7 [START_REF] Denlinger | Cutting Edge: The Nucleotide Receptor P2X 7 Contains Multiple Protein-and Lipid-Interaction Motifs Including a Potential Binding Site for Bacterial Lipopolysaccharide[END_REF] is involved in this effect has not been studied. Yet, activation of P2RX7 is associated with mechanisms and or cellular responses leading to growth suppressor evasion since tumors grew faster in p2rx7 -/-mice [START_REF] Hofman | Genetic and pharmacological inactivation of the purinergic P2RX7 receptor dampens inflammation but increases tumor incidence in a mouse model of colitis-associated cancer[END_REF][START_REF] Adinolfi | Accelerated tumor progression in mice lacking the ATP receptor P2X7[END_REF], an effect depending on host immune cells, as discussed below.

P2RX7 and cell death resistance

Resisting to apoptosis, a programmed cell death viewed as a natural barrier to cancer, is one of the characteristics of tumor cells. Depending on the cellular origin, the ATP/P2RX7 axis induces or inhibits apoptosis. In colorectal cancer cells a pharmacological inhibitor of P2RX7 induced apoptosis [START_REF] Zhang | PI3K/Akt/GSK-3β signal pathway is involved in P2X7 receptor-induced proliferation and EMT of colorectal cancer cells[END_REF]. In the MCF7 breast cancer model, decrease of P2RX7 expression with ShRNA induced cell apoptosis [START_REF] Tan | Expression of P2X7R in breast cancer tissue and the induction of apoptosis by the gene-specific shRNA in MCF-7 cells[END_REF]. By contrast, in acute myeloid leukemia, the ATP/P2RX7 axis induced apoptosis [START_REF] Salvestrini | Extracellular ATP induces apoptosis through P2X7R activation in acute myeloid leukemia cells but not in normal hematopoietic stem cells[END_REF]. Whether some evidence highlight a relationship between P2RX7induced signaling and apoptosis, it does not seem that these interactions act as a barrier to cancer. The same conclusion can be drawn for autophagy -an ancestral pathway which maintains cellular homeostasis by degrading long-lived proteins and removing unwanted or unnecessary intracellular components -which represents another cellular response that needs to be circumvented for tumor development. P2RX7-induced signaling pathway has been shown to negatively regulate the autophagic flux through the impairment of lysosomal functions in microglial cells [START_REF] Takenouchi | The Activation of P2X7 Receptor Impairs Lysosomal Functions and Stimulates the Release of Autophagolysosomes in Microglial Cells[END_REF]. This pathway, in association with HSP90, was also linked to autophagic cell death in myoblasts and was triggered by large pore formation [START_REF] Young | A novel mechanism of autophagic cell death in dystrophic muscle regulated by P2RX7 receptor large-pore formation and HSP90[END_REF]. In contrast, P2RX7-induced necrosis may be of particular relevance since it allows the release of growth factors which foster proliferation and invasiveness of cancer cells [START_REF] Giannuzzo | The P2X7 receptor regulates cell survival, migration and invasion of pancreatic ductal adenocarcinoma cells[END_REF], but also immunomodulatory cytokines as discussed below. Finally, it has been shown that tumor cells express a non-conformal receptor (nfP2RX7) which is unable to induce cell death but retains calcium channel activity and therefore could promote tumor growth [START_REF] Barden | Non-Functional P2X7: A Novel and Ubiquitous Target in Human Cancer[END_REF][START_REF] Gilbert | ATP in the tumour microenvironment drives expression of nfP2X7, a key mediator of cancer cell survival[END_REF]. The exact nature of this nfP2RX7 still remains an enigma but it may be linked to P2RX7B expression, as discussed in the P2RX7 and genomic instability chapter.

P2RX7 and replicative immortality

The unlimited replicative property of cancer cells is required to generate macroscopic tumor. P2RX7 could favor cell proliferation (see above), but whether P2RX7 could be linked to replicative immortality is currently unknown.

P2RX7 and angiogenesis

The formation of new vessels from pre-existing ones, called neo angiogenesis, is required to fuel tumors with oxygen and nutriments. The architecture of the tumor vasculature is abnormal, i.e. leaky, tortuous, fragile, and blind-ended with few or no perivascular cells. Such defectiveness leads to hypoxia and acidity in the TME, further reinvigorating tumor aggression. Therefore, normalization of abnormal tumor vasculature, resulting in enhanced drug delivery, is as important as inhibiting neovascularization. Angiogenesis is under the control of a growth factor called Vascular Endothelial Growth Factor (VEGF) which binds to the VEGF receptor (VEGFR2) to increase endothelial cell proliferation. P2RX7 has been reported to promote VEGF release from human monocytes and rat glioma cells [START_REF] Hill | Extracellular ATP May Contribute to Tissue Repair by Rapidly Stimulating Purinergic Receptor X7-Dependent Vascular Endothelial Growth Factor Release from Primary Human Monocytes[END_REF][START_REF] Wei | Expression and function of the P2X7 receptor in rat C6 glioma cells[END_REF].

Tumors obtained after engraftment of human embryonic kidney cells expressing P2RX7 are more angiogenic than tumors obtained with control cells [START_REF] Adinolfi | Expression of P2X7 receptor increases in vivo tumor growth[END_REF]. Mechanistically, it was proposed that the ATP/P2RX7 axis activates the PI3K/AKT and NFkB pathways which further lead to increased production of VEGF and increased-vessel density [START_REF] Adinolfi | Expression of P2X7 receptor increases in vivo tumor growth[END_REF][START_REF] Amoroso | The P2X7 receptor is a key modulator of the PI3K/GSK3β/VEGF signaling network: evidence in experimental neuroblastoma[END_REF][START_REF] Yang | P2X7R promotes angiogenesis and tumour-associated macrophage recruitment by regulating the NF-κB signalling pathway in colorectal cancer cells[END_REF]. Therefore, in tumor mice models, P2RX7 activation likely stimulates angiogenesis. However, to our knowledge no study in human cancer demonstrates a link between P2RX7 expression and neo angiogenesis.

P2RX7 and invasion and metastasis

Malignant cells possess the ability to invade surrounding tissues and metastasize. Importantly, the metastatic spread of the primary tumor accounts for over 90% of patient mortality associated with solid cancers [START_REF] Hanahan | Hallmarks of Cancer: The Next Generation[END_REF]. Metastasis is closely associated to the epithelialmesenchymal transition (EMT), a cellular response being under the control of transcription factors belonging to the Twist and Snail families, which in turn inhibit the expression of epithelial marker (E-cadherin) and increase the expression of mesenchymal markers (vimentin, N-cadherin) [START_REF] Lambert | Linking EMT programmes to normal and neoplastic epithelial stem cells[END_REF]. P2RX7 is highly expressed and fully functional in the highly invasive human breast cancer cells MDA-MB-435s; its activation leads to the acquisition of a pro migratory phenotype and enhanced cancer cell invasiveness in vitro and in vivo [START_REF] Jelassi | P2X7 receptor activation enhances SK3 channels-and cystein cathepsin-dependent cancer cells invasiveness[END_REF][START_REF] Jelassi | Anthraquinone emodin inhibits human cancer cell invasiveness by antagonizing P2X7 receptors[END_REF] thanks to the release of extracellular matrix degrading proteases. In lung cancer cells, the ATP/P2RX7 axis induced an autocrine regulation of TGF-β-induced cell migration [START_REF] Takai | Autocrine regulation of TGF-β1-induced cell migration by exocytosis of ATP and activation of P2 receptors in human lung cancer cells[END_REF]. Further, P2RX7 effect on cell migration and invasion was observed in response to ATP in pancreatic ductal adenocarcinoma cells [START_REF] Giannuzzo | The P2X7 receptor regulates cell survival, migration and invasion of pancreatic ductal adenocarcinoma cells[END_REF], but also in the human T47D breast cancer cells and colon cancer cells, where activation of the PI3K/AKT/GSK3 pathway in response to ATP regulates Ecadherin expression and EMT [START_REF] Zhang | PI3K/Akt/GSK-3β signal pathway is involved in P2X7 receptor-induced proliferation and EMT of colorectal cancer cells[END_REF][START_REF] Xia | P2X7 receptor stimulates breast cancer cell invasion and migration via the AKT pathway[END_REF]. Whereas many reports highlight the ability of P2RX7 to induce cell invasion and migration in cultured tumor cells, only one study demonstrates its implication in metastasis, considering that metastases correspond to secondary tumors growing at distance of the primary tumor. Using the mammary 4T1 cancer mouse model, it was shown that P2RX7 expression promotes tumor growth and metastasis development, both of them being reduced when mice were treated with specific P2RX7 antagonists [START_REF] Brisson | P2X7 Receptor Promotes Mouse Mammary Cancer Cell Invasiveness and Tumour Progression, and Is a Target for Anticancer Treatment[END_REF].

Mechanistically, P2X7R expression enhances the ECM-degradative activity of invadopodia and triggers the acquisition of a more aggressive phenotype, characterized by activation of Cdc42Rho-GTPase, remodeling of F-actin, elongation of cells and formation of filopodia.

Further, a higher expression of P2RX7 has been documented in bronchoalveolar lavage cells of tumors with distant metastasis [START_REF] Schmid | Altered purinergic signaling in the tumor associated immunologic microenvironment in metastasized non-small-cell lung cancer[END_REF] and in B16 melanoma-induced lung metastasis in mice.

However and in contradiction with the above findings, more lung tumors were observed with B16 melanoma cells injected in p2rx7 -/-mice, suggesting that endogenous P2RX7 activity held by host cells restrains B16 migration and invasion [START_REF] Adinolfi | Accelerated tumor progression in mice lacking the ATP receptor P2X7[END_REF]. Undoubtedly, additional experiments are required to fully appreciate the role of P2RX7 in invasion and metastasis depending on the cancer origin.

P2RX7 and genomic instability

Genomic instability of cancer cells favors the selection of subclones demonstrating growth advantage and nowadays study of tumor mutational load is proposed as a sensitive and quantitative clinical marker that can help predict responses to cancer treatment. To our knowledge, P2RX7 was not reported to directly induce DNA mutations. However, several number of missense mutations are present in human P2RX7 and until now 16 Single Nucleotide Polymorphisms (SNPs) have been characterized in the coding region of human P2RX7 gene [START_REF] Sluyter | Significance of p2x7 receptor variants to human health and disease[END_REF]. These mutations affect P2RX7 activity. For instance, the polymorphism in exon 13 at the +1513 position (Glu496Ala substitution, corresponding to SNP rs3751143) has been shown to eradicate the function of this receptor and to be associated with papillary thyroid cancer, hepatocarcinoma and chronic lymphocytic leukemia [START_REF] Cabrini | A His-155 to Tyr Polymorphism Confers Gain-of-Function to the Human P2X 7 Receptor of Human Leukemic Lymphocytes[END_REF][START_REF] Dardano | 1513A>C Polymorphism in the P2X7 Receptor Gene in Patients with Papillary Thyroid Cancer: Correlation with Histological Variants and Clinical Parameters[END_REF][START_REF] Duan | Association Between P2RX7 Gene and Hepatocellular Carcinoma Susceptibility: A Case-Control Study in a Chinese Han Population[END_REF][START_REF] Virgilio | The P2X7 receptor of CLL lymphocytes-a molecule with a split personality[END_REF]. However, a recent meta-analysis studying the link between rs3751143 and cancer risk minored these results [START_REF] Wang | Predicted the P2RX7 rs3751143 polymorphism is associated with cancer risk: a meta-analysis and systematic review[END_REF]. Nevertheless, in the field of hematological malignancies, P2RX7 and its genetic variants are now considered as biomarkers and potential therapeutic tools [START_REF] Marchi | P2X7 Receptor in Hematological Malignancies[END_REF].

In non-small cell lung cancer P2RX7 expression and the SNP rs3751143 does not impact tumor onset [START_REF] Boldrini | P2X7 protein expression and polymorphism in non-small cell lung cancer (NSCLC)[END_REF], whereas this SNP was associated with reduced metastatic free survival in anthracyclines treated breast cancer [START_REF] Ghiringhelli | Activation of the NLRP3 inflammasome in dendritic cells induces IL-1Β-dependent adaptive immunity against tumors[END_REF]. On the contrary, a genetic statistical interaction between P2RX7 (rs3751143, rs208294) and VEGFR-2 (rs2071559, rs11133360) genotypes may identify a population of prostate cancer patients with a better prognosis [START_REF] Solini | Genetic interaction of P2X7 receptor and VEGFR-2 polymorphisms identifies a favorable prognostic profile in prostate cancer patients[END_REF]. In addition to SNPs, the pre-messenger RNA of P2RX7 is subjected to alternative splicing leading to the formation of 12 P2RX7 mRNA variants [START_REF] Benzaquen | Alternative splicing of P2RX7 pre-messenger RNA in health and diseases: Myth or reality?[END_REF][START_REF] Cheewatrakoolpong | Identification and characterization of splice variants of the human P2X7 ATP channel[END_REF][START_REF] Skarratt | A P2RX7 single nucleotide polymorphism haplotype promotes exon 7 and 8 skipping and disrupts receptor function[END_REF]. The function of only four of them, P2RX7B with a deletion in the C-terminal domain (ΔCt) leading to a loss of large pore forming function, P2RX7H with a deletion in the first transmembrane domain (ΔTM1) leading to a loss of ion channel function, P2RX7J with a deletion in the second transmembrane domain leading to a negative dominant isoform and the recently described P2RX7L, characterized by skipping of exons 7 and 8 leading to deletion of amino acids in the ATP binding site, have been studied in more details [START_REF] Adinolfi | Trophic activity of a naturally occurring truncated isoform of the P2X7 receptor[END_REF][START_REF] Feng | A truncated P2X7 receptor variant (P2X7-j) endogenously expressed in cervical cancer cells antagonizes the full-length P2X7 receptor through hetero-oligomerization[END_REF][START_REF] Skarratt | A P2RX7 single nucleotide polymorphism haplotype promotes exon 7 and 8 skipping and disrupts receptor function[END_REF][START_REF] Giuliani | Trophic activity of human P2X7 receptor isoforms A and B in osteosarcoma[END_REF][100]. The truncated P2RX7J was first described to antagonize P2RX7A function in cancer cervical cells, this effect was associated with apoptosis-inhibitory actions [START_REF] Feng | A truncated P2X7 receptor variant (P2X7-j) endogenously expressed in cervical cancer cells antagonizes the full-length P2X7 receptor through hetero-oligomerization[END_REF]. More recently, the P2RX7B splice variant has been shown to be differentially upregulated in immune cells of human Non-Small Cell Lung Cancer. This overexpression correlates with a default in the activity of P2RX7 in immune cells and lung tumors expressing P2RX7B are less inflamed. Therefore, considering that expression of P2RX7B in lung tumors correlated with both an alteration in the P2RX7 function and the lesser infiltrated tumor phenotype (also called "cold" tumor), it was proposed that P2RX7B participates in lung tumor development [101]. A similar conclusion was drawn in patients with AML. In those patients expression levels of both P2RX7A and B mRNA are increased and daunorubicin, the main chemotherapeutic for AML, only reduced the expression levels of P2RX7A, whereas P2RX7B levels remained high, which correlate with cells more prone to survive [102]. One may hypothesize that the heterotrimers composed of P2RX7A and P2RX7B isoforms may correspond to the nfP2RX7, since they shared the same biological activities e.g., no large pore formation and low Ca2+ channel activity [START_REF] Gilbert | ATP in the tumour microenvironment drives expression of nfP2X7, a key mediator of cancer cell survival[END_REF].

P2RX7 and reprogramming energy metabolism

P2RX7 and immune responses related to tumor onset

Beside efficiently fighting infections, the immune system is also programmed to eliminate cancer cells that ultimately evade immune surveillance via checkpoint inhibitor expression and secretion of immunosuppressive factors but also survival and migration factors. [107, 108]. As discussed above, P2RX7's functions have been extensively studied in immune cells. We will focus here on P2RX7's immune populations known to be involved in tumor onset where p2rx7 -/-mice developed more tumors and tumors were more infiltrated with Tregs than tumors grown in WT mice [START_REF] Hofman | Genetic and pharmacological inactivation of the purinergic P2RX7 receptor dampens inflammation but increases tumor incidence in a mouse model of colitis-associated cancer[END_REF]127]. Such results could be explained by the fact that P2RX7 controls Treg homeostasis and in absence of P2RX7, Treg death is decreased [128,129].

Finally, whereas the level of P2RX7 expression in human MDSCs has not yet been reported, it was shown in the neuroblastoma mice model that the ATP/P2RX7 axis increases immunosuppressive functions of MDSCs [130]. In conclusion, while it is indisputable that P2RX7 modulates immune cell functions, the contradicting results found in the literature make it is difficult to predict whether P2RX7, by itself, impinges or enhances antitumor immunes responses.

Should we target P2RX7 in cancer biology?

This is indeed THE QUESTION, and as evidenced in previous chapters, this is a puzzling question (Figure 3). Should we inhibit P2RX7 in order to blunt its proliferative effect on cancer cells? However, this scenario may be dangerous since it may also inhibit antitumor immune responses sustained by P2RX7? Should we activate P2RX7 in order to boost its cytotoxic effect on tumor cells? But is P2RX7 really cytotoxic on tumor cells, since we know that many tumors express a nfP2RX7? If we activate P2RX7, would it really enhance antitumor immune responses or would it boost the function of immunosuppressive cells that are recruited in the TME?

Since the notion of immunosurveillance emerges only recently in the field of cancer biology

[131] and because big pharma were more prone to develop inhibitors, especially P2RX7 antagonists that may treat inflammatory disorders, numerous small pharmacological inhibitors and biologics were tested in preclinical and clinical studies as reviewed in [START_REF] Lara | P2X7 in Cancer: From Molecular Mechanisms to Therapeutics[END_REF]. In brief, most of the preclinical studies were based on transplantation in immunodeficient or syngeneic immunocompetent mice of various tumor cell lines (e.g., HEK293, neuroblastoma, colon carcinoma, melanoma, glioma, osteosarcoma and myeloid leukemia) endowed with endogenous or exogenous expression of P2RX7, where tumor growth was significantly reduced by treatment with AZ10606120, A438079 or A740003, three well described antagonists of P2RX7 [START_REF] Adinolfi | Expression of P2X7 receptor increases in vivo tumor growth[END_REF][START_REF] Giannuzzo | The P2X7 receptor regulates cell survival, migration and invasion of pancreatic ductal adenocarcinoma cells[END_REF][START_REF] Zhang | Highly-expressed P2X7 receptor promotes growth and metastasis of human HOS/MNNG osteosarcoma cells via PI3K/Akt/GSK3β/βcatenin and mTOR/HIF1α/VEGF signaling[END_REF][START_REF] Adinolfi | Accelerated tumor progression in mice lacking the ATP receptor P2X7[END_REF]127,132]. However, an acceleration of tumor growth was also described when tumor cell lines were transplanted in p2rx7 KO mice or with chemically -induced in situ colon tumors [START_REF] Hofman | Genetic and pharmacological inactivation of the purinergic P2RX7 receptor dampens inflammation but increases tumor incidence in a mouse model of colitis-associated cancer[END_REF][START_REF] Adinolfi | Accelerated tumor progression in mice lacking the ATP receptor P2X7[END_REF]. These results highlight the importance of P2RX7 expressing host cells in tumor onset and suggest that the levels of expression and functionality of P2RX7 in tumor cells is a critical issue that have to be considered before the use of antagonists to treat tumor. As far as we know, no clear benefits to patients have been reported so far and this lack of effect may be related to the fact that inflammatory tissues contain high levels of ATP, a condition that does not favor the binding of antagonist to the allosteric binding site [133]. The use of biologics may be an alternative. In particular, application of an antibody that specifically recognizes the E200 epitope of nfP2RX7 appears to be promising for the local treatment of the primary basal cell carcinoma lesion [134].

We hypothesized that increasing P2RX7's dependent antitumor immune response by enhancing its activation would inhibit tumor growth. We developed positive allosteric modulators (PAMs) of P2RX7, i.e. molecules that enhances P2RX7 activity in the presence of ATP. HEI3090 was selected as the most potent PAM that enhances P2RX7's activities only in the presence of ATP and showed that it efficiently inhibits tumor growth.

Perspectives

Undoubtably, targeting P2RX7 to treat cancer is a promising strategy and recent data from several laboratories highlighted the capacity of the ATP/P2RX7/NLRP3/IL-18 axis to activate antitumor immune responses. However, pivotal questions remain to be answered. Among them, how do the new pharmacological positive modulator of P2RX7 (HEI3090) enhances the receptor activity? Does it bind to the receptor itself and if so, where? Is HEI3090 active on the nfP2RX7? And if so, which and how, P2RX7-dependent biological responses are going to be regulated? Would it be interesting to combine HEI3090 with the new anti CD39 antibody in order to maximize the activation of the ATP/P2RX7/NLRP3/IL-18 axis and the consecutive antitumor immune response? Would the other non-nucleotide agonists of P2RX7 (i.e. ginsenoside, LL37 [138, 139] will also boost the antitumor immune responses supported by the ATP/P2RX7 axis? and if so, could we combine them with HEI3090 and immunotherapies?

What would be the effect of an agonist of P2RX7 on inflammatory pain and IL-1β-induced cachexia, knowing that P2RX7 antagonists not only efficiently inhibit the production of IL-1β but also reduced cancer-induced bone pain [140, 141]? In this perspective, the use of HEI3090 may be of particular interest, since we showed in transplantable and oncogene-driven tumor models that it increases seric IL-18 levels with no effect on IL-1β. We believe that these points will be resolved in a near future, and in addition, we are strongly convinced that their resolution will bring new innovative strategies to fight cancers.

Otto

  Warburg was the first to described that cancer cells can reprogram their glucose metabolism, thanks to glycolysis, even in the presence of oxygen [103]. In the heterotopic HEK293 cell system, expression of P2RX7 leads to the regulation of numerous glycolytic markers conferring to this receptor the power to control cellular energy metabolism [104]. In a human cohort which includes more refined measures of glucose homeostasis, P2RX7 has been linked to glucose regulation [105]. Accordingly, it was shown that P2RX7 loss of function in mice produces defective energy homeostasis [106]. To our knowledge, no study demonstrated that P2RX7 directly regulates energy metabolism of cancer cells. Yet, numerous important studies link P2RX7 to immunometabolism as discussed below.

  [109]. An efficient anticancer immune response is normally sufficient to kill cancer cells. On one hand, soluble neoantigens express by cancer cells are recognized by circulating Antigen Presenting Cells (APC) such as dendritic cells (DCs) and macrophages. Then, APC present the tumor antigens on MHC-I and MHC-II molecules to T cells (CD8+, CD4+) leading to priming and activation of effector T cell responses which traffic to tumor bed, recognize tumor antigen and kill tumor cells. On the other hand, the Natural Killer (NK) cells can directly kill tumor cells by using cytolytic granules and death receptors and by potentiating adaptive immunity through the production of cytokines such as Interferon gamma (IFN-) [110]. These antitumor immune responses are blunted by immunosuppressive cells such as regulatory T cells (Treg), and myeloid derived suppressor cells (MDSC) [111]. Both macrophages and DCs from human origin express P2RX7 which is able to modulate cytokine production in response to eATP and LPS [112-115]. The potential of the ATP/P2RX7 axis to enhance antitumor immune responses was suggested by Kroemer's team after the demonstration that eATP attracts DC precursors into the tumor bed, facilitates their permanence in the proximity of dying cells and promotes their differentiation into mature DCs endowed with the capacity of presenting tumor-associated antigens [116]. In addition, P2RX7 activation in macrophages and DCs induces NLRP3 leading to increased production of IL-1β and IL-18. Both pro-tumorigenic and anti-tumorigenic roles of NLRP3 inflammasome have been described in cancers [117]. This double-edged sword effect may depend on the quantity of cytokine produced. For instance, more IL-18 will trigger a more efficient antitumor immune response [118]. Recent studies involving patient with acute myeloid leukemia revealed that P2RX7 mRNAs (A and B) are upregulated in immune cancer versus immune non cancer cells and AML patients with higher levels of P2RX7 mRNA show a lower survival rate [102, 119]. Further, engraftment of human myeloid cell lines in immunodeficient mice leads to tumor growth and treatment with specific antagonists of P2RX7 decreases the size of the tumors and increases mice survival rate. The importance of the ATP/P2RX7 axis in leukemic models was further improved by the discovery that P2RX7 is highly expressed in leukemia initiating cells where it promotes AML development by sustaining Ca2+-dependent cell signaling [120]. Human lymphocytes also express P2RX7 and demonstrated a growth promoting activity [121, 122]. Of note, at least in mice, P2RX7 expression levels is heterogenous across immune cells. Indeed, it is highly expressed by macrophages, DCs and monocytes while NK cells, CD8+ and CD4+foxp3-T cells, neutrophils and eosinophils express very low levels [123]. Yet, it was shown that P2RX7 is required for the establishment, maintenance and functionality of longlived memory CD8+ T cells (TRM), an effect that depends on P2RX7-dependent metabolic fitness [124]. Interestingly, in a melanoma mice model, TRM can actively suppress cancer progression [125]. P2RX7 is also expressed by human Tregs. It was shown that esophageal carcinoma from patients expressing the loss-of-function allele of P2RX7 are more infiltrated with Tregs than tumors from patients bearing the WT allele of P2RX7 [126]. The same tendency was observed in the colitis associated cancer mice model and melanoma models,

  HEI3090 enhances antitumor immune responses by boosting the ATP/P2RX7/NLRP3/IL-18 pathway in DCs, sensitizes tumor to anti PD-1 immunotherapy and triggers long lasting antitumor immune responses [135]. Our bet looks like a successful story, which is not so surprising considering that anti CD39 antibody was recently shown to promote the eATP/P2RX7/NLRP3/IL18-driven antitumor responses [123]. In addition, activation of this pathway with anti CD39 monoclonal antibody in myeloid cells activates effector function of NK cells and suppresses experimental and spontaneous metastases in a tumor of different origins [136], therefore bringing new hopes for the treatment of cancers. However, more work is needed to understand which immune cells should be targeted to kill tumor cells in order to reconciliate data showing a benefit to activate the ATP/P2RX7 axis in specific immune cells (macrophages, DCs, NK) and data showing that the lack of P2RX7 in tumor-specific T cells reduced tumor growth [137]. Further, we need to understand why tumor fate is different in p2rx7 null mice and mice treated with an antagonist of P2RX7 [127].

Figure 1 :

 1 Figure 1 : P2RX7 expression in eukaryotic cells

Figure 2 :

 2 Figure 2 : P2RX7-induced cell responses related to hallmarks of cancer

Figure 3 :

 3 Figure 3 : The yin and yang of P2RX7's modulators

  Figure 3

Funding VV-C was supported by grants from Institut National du Cancer (INCa # 2015-137), Canceropole PACA, Bristol-Myers Squibb Foundation for Research in Immuno-Oncology, the French Government (National Research Agency, ANR through the "Investments for the Future": program reference #ANR-11-LABX-0028-01) and the Centre National de la Recherche Scientifique (CNRS). SJH was supported by the "Ligue Nationale Contre le Cancer" and the "Fondation pour la recherche médicale # FDT202106013099" JB was supported by the "Plan Cancer: Formation à la recherche Translationnelle en cancérologie ».

Conflicts of interest:

All authors declare they have no conflicts of interest.

Ethical approval: This review does not contain any studies with participants or animals performed by any of the authors.


Hao Chen
email: hao.chen@inria.fr

Yaohui Wang
email: yaohui.wang@inria.fr

Benoit Lagadec
email: benoit.lagadec@esifrance.net

Antitza Dantcheva
email: antitza.dantcheva@inria.fr

Francois Bremond
email: francois.bremond@inria.fr

Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a meshbased view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.

Introduction

A person re-identification (ReID) system is targeted at identifying subjects across different camera views. In particular, given an image containing a person of interest (as query) and a large set of images (gallery set), a ReID system ranks gallery-images based on visual similarity with the query. Towards this, ReID systems are streamlined to bring to the fore discriminative representations, which allow for robust comparison of query and gallery images. In this context, supervised ReID methods [4,[START_REF] Sun | Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)[END_REF] learn representations guided by human-annotated labels, which is time-consuming and cumbersome. Towards omitting such human annotation, researchers increasingly place emphasis on unsupervised person ReID algorithms [START_REF] Wang | Unsupervised person reidentification via multi-label classification[END_REF]24,[START_REF] Lin | A bottom-up clustering approach to unsupervised person re-identification[END_REF], which learn directly from unlabeled images and thus allow for scalability in real world deployments. Recently, self-supervised contrastive methods [START_REF] He | Momentum contrast for unsupervised visual representation learning[END_REF]6] have provided an effective retrieval-based approach for unsupervised representation learning. Given an image, such methods maximize agreement between two augmented views of one instance (see Fig. 1). Views refer to transformed versions of the same input. As shown in very recent works [6,[START_REF] Chen | Improved baselines with momentum contrastive learning[END_REF], data augmentation enables a network to explore view-invariant features by providing augmented views of a person, which are instrumental in building robust representations. Such and similar methods considered traditional data augmentation techniques, e.g., 'random flipping', 'cropping', and 'color jittering'. Generative Adversarial Networks (GANs) [15] constitute a novel approach for data augmentation. As opposed to traditional data augmentation, GANs are able to modify id-unrelated features substantially, while preserving id-related features, which is highly beneficial in contrastive ReID.

Previous GAN-based methods [START_REF] Bak | Domain adaptation through synthesis for unsupervised person re-identification[END_REF][START_REF] Deng | Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[END_REF][START_REF] Zou | Joint disentangling and adaptation for cross-domain person re-identification[END_REF][START_REF] Li | Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation[END_REF][START_REF] Wei | Person transfer gan to bridge domain gap for person reidentification[END_REF][START_REF] Zhong | Generalizing a person retrieval model hetero-and homogeneously[END_REF]] considered unsupervised ReID as an unsupervised domain adaptation (UDA) problem. Under the UDA setting, researchers used both, a labeled source dataset, as well as an unlabeled target dataset to gradually adjust a model from a source domain into a target domain. GANs can be used in cross-domain style transfer, where labeled source domain images are generated in the style of a target domain. However, the UDA setting necessitates a large-scale labeled source dataset. Scale and quality of the source dataset 1 strongly affect the performance of UDA methods. Recent research has considered fully unsupervised ReID [START_REF] Wang | Unsupervised person reidentification via multi-label classification[END_REF]24], where under the fully unsupervised setting, a model directly learns from unlabeled images without any identity labels. Self-supervised contrastive methods [START_REF] He | Momentum contrast for unsupervised visual representation learning[END_REF]6] belong to this category. In this work, we use a GAN as a novel view generator for contrastive learning, which does not require a labeled source dataset.

Here, we aim at enhancing view diversity for contrastive learning via generation under the fully unsupervised setting. Towards this, we introduce a mesh-based novel view generator. We explore the possibility of disentangling a person image into identity features (color distribution and body shape) and structure features (pose and view-point) under the fully unsupervised ReID setting. We estimate 3D meshes from unlabeled training images, then rotate these 3D meshes to simulate new structures. Compared to skeleton-guided pose transfer [START_REF] Ge | Fd-gan: Pose-guided feature distilling gan for robust person re-identification[END_REF][START_REF] Li | Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation[END_REF], which neglects body shape, mesh recovery [START_REF] Kanazawa | End-to-end recovery of human shape and pose[END_REF] jointly estimates pose and body shape. Estimated meshes preserve body shape during the training, which facilitates the generation and provides more visual clues for fine-grained ReID. Novel views can be generated by combining identity features with new structures.

Once we obtain the novel views, we design a pseudo label based contrastive learning module. With the help of our proposed view-invariant loss, we maximize representation similarity between original and generated views of a same person, whereas representation similarity of other persons is minimized.

Our proposed method incorporates generative and contrastive modules into one framework, which are trained jointly. Both modules share the same identity feature encoder. The generative module disentangles identity and structure features, then generates diversified novel views. The novel views are then used in the contrastive module to improve the capacity of the shared identity feature encoder, which in turn improves the generation quality. Both modules work in a mutual promotion way, which significantly enhances the performance of the shared identity feature encoder in unsupervised ReID. Moreover, our method is compatible with both UDA and fully unsupervised settings. With a labeled source dataset, we obtain better performance by alleviating the pseudo label noise.

Our contributions can be summarized as follows.

1. We propose a joint generative and contrastive learning framework for unsupervised person ReID. Generative and contrastive modules mutually promote each other's performance.

2. In the generative module, we introduce a 3D mesh based novel view generator, which is more effective in body shape preservation than skeleton-guided generators.

3. In the contrastive module, a view-invariant loss is proposed to reduce intra-class variation between original and generated images, which is beneficial in building view-invariant representations under a fully unsupervised ReID setting.

4. We overcome the limitation of previous GAN-based unsupervised ReID methods that strongly rely on a labeled source dataset. Our method significantly surpasses the performance of state-of-the-art methods under both, fully unsupervised, as well as UDA settings.

Related Work

Unsupervised representation learning. Recent contrastive instance discrimination methods [START_REF] Wu | Unsupervised feature learning via non-parametric instance discrimination[END_REF][START_REF] He | Momentum contrast for unsupervised visual representation learning[END_REF]6] have witnessed a significant progress in unsupervised representation learning. The basic idea of instance discrimination has to do with the assumption that each image is a single class. Contrastive predictive coding (CPC) [START_REF] Van Den Oord | Representation learning with contrastive predictive coding[END_REF] included an InfoNCE loss to measure the ability of a model to classify positive representation amongst a set of unrelated negative samples, which has been commonly used in following works on contrastive learning. Recent contrastive methods treated unsupervised representation learning as a retrieval task. Representations can be learnt by matching augmented views of a same instance from a memory bank [START_REF] Wu | Unsupervised feature learning via non-parametric instance discrimination[END_REF][START_REF] He | Momentum contrast for unsupervised visual representation learning[END_REF] or a large mini-batch [6]. MoCoV2 [START_REF] Chen | Improved baselines with momentum contrastive learning[END_REF] constitutes the improved version of the MoCo [START_REF] He | Momentum contrast for unsupervised visual representation learning[END_REF] method, incorporating larger data augmentation. We note that data augmentation is pertinent in allowing a model to learn robust representations in contrastive learning. However, only traditional data augmentation was used in aforementioned methods.

Data augmentation. MoCoV2 [START_REF] Chen | Improved baselines with momentum contrastive learning[END_REF] used 'random crop', 'random color jittering', 'random horizontal flip', 'random grayscale' and 'gaussian blur'. However, 'random color jittering' and 'grayscale' were not suitable for finegrained person ReID, because such methods for data augmentation tend to change the color distribution of original images. In addition, 'Random Erasing' [START_REF] Zhong | Random erasing data augmentation[END_REF] has been a commonly used technique in person ReID, which randomly erases a small patch from an original image. Crossdomain Mixup [START_REF] Luo | Generalizing person re-identification by camera-aware invariance learning and cross-domain mixup[END_REF] interpolated source and target domain images, which alleviated the domain gap in UDA ReID. Recently, Generative Adversarial Networks (GANs) [15] have shown great success in image [START_REF] Karras | Analyzing and improving the image quality of StyleGAN[END_REF][START_REF] Karras | A style-based generator architecture for generative adversarial networks[END_REF]2] and video synthesis [START_REF] Tulyakov | MoCoGAN: Decomposing motion and content for video generation[END_REF][START_REF] Wang | G3AN: Disentangling appearance and motion for video generation[END_REF][START_REF] Chan | Everybody dance now[END_REF][START_REF] Wang | Inmodegan: Interpretable motion decomposition generative adversarial network for video generation[END_REF][START_REF] Wang | Imaginator: Conditional spatio-temporal gan for video generation[END_REF]. GAN-based methods can serve as a method for evolved data augmentation by conditionally modifying id-unrelated features (style and structure) for supervised ReID. CamStyle [START_REF] Zhong | Camera style adaptation for person reidentification[END_REF] used the CycleGANarchitecture [START_REF] Zhu | Unpaired image-to-image translation using cycleconsistent adversarial networks[END_REF] in order to transfer images from one camera into the style of another camera. FD-GAN [START_REF] Ge | Fd-gan: Pose-guided feature distilling gan for robust person re-identification[END_REF] was targeted to generate images in a pre-defined pose, so that images could be compared in the same pose. IS-GAN [START_REF] Eom | Learning disentangled representation for robust person re-identification[END_REF] was streamlined to disentangle id-related and id-unrelated features by switching both local and global level identity features. DG-Net [START_REF] Zheng | Joint discriminative and generative learning for person re-identification[END_REF] recolored grayscale images with a color distribution of other images, targeting to disentangle identity features. Deviating from such supervised GAN-based methods, our method generates novel views by rotating 3D meshes in an unsupervised manner.

Unsupervised person ReID. Recent unsupervised person ReID methods were predominantly based on UDA. Among UDA-based methods, several works [36,[START_REF] Lin | Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification[END_REF] used semantic attributes to facilitate domain adaptation. Other works [START_REF] Wu | Progressive learning for person re-identification with one example[END_REF]12,5,[START_REF] Yang | Asymmetric co-teaching for unsupervised cross-domain person re-identification[END_REF][START_REF] Ge | Mutual meanteaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification[END_REF] assigned pseudo labels to unlabeled images and proceeded to learn representations with pseudo labels. Transferring source dataset images into the style of a target dataset represents another line of research. SP-GAN [START_REF] Deng | Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[END_REF] and PTGAN [START_REF] Wei | Person transfer gan to bridge domain gap for person reidentification[END_REF] used CycleGAN [START_REF] Zhu | Unpaired image-to-image translation using cycleconsistent adversarial networks[END_REF] as domain style transfer-backbone. HHL [START_REF] Zhong | Generalizing a person retrieval model hetero-and homogeneously[END_REF] aims at transferring cross-dataset camera styles. ECN [START_REF] Zhong | Invariance matters: Exemplar memory for domain adaptive person re-identification[END_REF][START_REF] Zhong | Learning to adapt invariance in memory for person re-identification[END_REF] exploited invariance from camera style transferred images for UDA ReID. CR-GAN [START_REF] Chen | Instanceguided context rendering for cross-domain person reidentification[END_REF] employed parsing-based masks to remove noisy backgrounds. PDA [START_REF] Li | Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation[END_REF] included skeleton estimation to generate person images with different poses and cross-domain styles. DG-Net++ [START_REF] Zou | Joint disentangling and adaptation for cross-domain person re-identification[END_REF] jointly disentangled id-related/id-unrelated features and transferred domain styles. While the latter is related to our method, we aim at training jointly a GAN-based online data augmentation, as well as a contrastive discrimination, which renders the labeled source dataset unnecessary, rather than transferring style. Fully unsupervised methods do not require any identity labels. BUC [START_REF] Lin | A bottom-up clustering approach to unsupervised person re-identification[END_REF] represented each image as a single class and gradually merged classes. In addition, TSSL [START_REF] Wu | Tracklet self-supervised learning for unsupervised person reidentification[END_REF] considered each tracklet as a single class to facilitate cluster merging. SoftSim [START_REF] Lin | Unsupervised person re-identification via softened similarity learning[END_REF] utilized similarity-based soft labels to alleviate label noise. MMCL [START_REF] Wang | Unsupervised person reidentification via multi-label classification[END_REF] assigned multiple binary labels and trained a model in a multi-label classification way. JVTC and JVTC+ [24] added temporal information to refine visual similarity based pseudo labels. We note that all aforementioned fully unsupervised methods learn from pseudo labels. We show in this work that disentangling view-invariant identity features is possible in fully unsupervised ReID, which can be an add-on to boost the performance of previous pseudo label based methods.

Proposed Method

We refer to our proposed method as joint Generative and Contrastive Learning as GCL. The general architecture of GCL comprises of two modules, namely a View Generator, as well as a View Contrast Module, see Fig. 2. Firstly, the View Generator uses cycle-consistency on both, image and feature reconstructions in order to disentangle identity and structure features. It combines identity features and mesh-guided structure features to generate one person in new view-points. Then, original and generated views are exploited as positive pairs in the View Contrast Module, which enables our network to learn view-invariant identity features. We proceed to elaborate on both modules in the following.

View Generator (Generative Module)

As shown in Fig. 2, the proposed View Generator incorporates 4 networks: an identity encoder E id , a structure encoder E str , a decoder G and an image discriminator D. Given an unlabeled person ReID dataset X = {x 1 , x 2 , ..., x N }, we generate corresponding 3D meshes with a popular 3D mesh generator Human Mesh Recovery (HMR) [START_REF] Kanazawa | End-to-end recovery of human shape and pose[END_REF], which simultaneously estimates body shape and pose from a single RGB image. Here, we denote the 2D projection of a 3D mesh as original structure s ori . Then, as depicted in Fig. 3, we rotate each 3D mesh by 45 °, 90 °, 135 °, 180 °, 225 °, 270 °and 315 °, respectively and proceed to randomly pick one 2D projection of these rotated meshes as a new structure s new . We use the 3D mesh rotation to mimic view-point variance from different cameras. Next, unlabeled images are encoded to identity features by the identity encoder E id : x → f id , while both original and new structures are encoded to structure features by the structure encoder E str : s ori → f str(ori) , s new → f str(new) . Combining both, identity and structure features, the decoder generates synthesized images G : (f id , f str(ori) ) → x ori , (f id , f str(new) ) → x new , where a prime is used to represent generated images.

Given the lack of real images corresponding to the new structures, we consider a cycle consistency [START_REF] Zhu | Unpaired image-to-image translation using cycleconsistent adversarial networks[END_REF] to reconstruct the original image by swapping the structure features in the View Generator. We encode and decode once again to get synthesized images in original structures G(E id (x new ), s ori ) → x ori . We calculate an image reconstruction loss as follows.

L img = E[ x -x ori 1 ] + E[ x -x ori 1 ] (1)
In addition, we compute a feature reconstruction loss

L f eat = E[ f id -E id (x new ) 1 ]+ E[ f id -E id (x ori ) 1 ]. (2)
The discriminator D attempts to distinguish between real and generated images with the adversarial loss

L adv = E[log D(x) + log(1 -D(x ori ))]+ E[log D(x) + log(1 -D(x new ))]+ E[log D(x) + log(1 -D(x ori ))]. (3)
Consequently, the overall GAN loss combines the above named losses with weighting coefficients λ img and λ f eat

L gan = λ img L img + λ f eat L f eat + L adv . (4)

View Contrast (Contrastive Module)

The previous reconstruction and adversarial losses work in an unconditional manner. They only explore identity features within the original view-point, which renders appearance representations view-variant. In rotating an original mesh to a different view-point, e.g., from front to side viewpoint, the generation is prone to fail due to lack of information pertained to the side view. This issue can be alleviated by enhancing the view-invariance of representations.

Given an anchor image x, the first step is to find positive images that belong to the same identity and negative images that belong to different identities. Here, we store all instance representations in a memory bank [START_REF] Wu | Unsupervised feature learning via non-parametric instance discrimination[END_REF], which stabilizes pseudo labels and enlarges the number of negatives during the training with mini-batches. The memory bank M is updated with a momentum coefficient α.

M[i] t = α • M[i] t-1 + (1 -α) • f t (5)
where M[i] t and M[i] t-1 respectively refer to the identity feature vector in the t and t -1 epochs. We use a clustering algorithm DBSCAN [START_REF] Ester | A density-based algorithm for discovering clusters in large spatial databases with noise[END_REF] on all memory bank feature vectors to generate pseudo identity labels Y = {y 1 , y 2 , ..., y J }, which are renewed at the beginning of every epoch. Given the obtained pseudo labels, we have N pos positive and N neg negative instances for each training instance. N pos and N neg vary for different instances. For simplicity in a mini-batch training, we fix common positive and negative numbers for every training instance. Given an image x, we randomly sample K instances that have different pseudo identities and one instance representation f pos that has the same pseudo identity with x from the memory bank. Note that f pos is from a random positive image that usually has a pose and camera style different from x and x new . x and x new are encoded by E id into identity feature vectors f and f new . Next, f , f new and f pos are used in turn to form three positive pairs. The f new and K different identity instances in the memory bank are used as K negative pairs. Towards learning robust viewinvariant representations, we extend the InfoNCE loss [START_REF] Van Den Oord | Representation learning with contrastive predictive coding[END_REF] into a view-invariant loss between original and generated views. We use

sim(u, v) = u u 2 • v v 2
to denote the cosine similarity. We define the view-invariant loss as a softmax log loss of K + 1 pairs as following.

L vi = E[log (1 + K i=1 exp (sim(f new , k i )/τ ) exp (sim(f, f pos )/τ ) )] (6)
L vi = E[log (1 + K i=1 exp (sim(f new , k i )/τ ) exp (sim(f new , f )/τ ) )] (7)
L vi = E[log (1 + K i=1 exp (sim(f new , k i )/τ ) exp (sim(f new , f pos )/τ ) )], (8)
where τ indicates a temperature coefficient that controls the scale of calculated similarities. L vi maximizes the invariance between original and memory positive views. L vi maximizes the invariance between synthesized and original views. L vi maximizes the invariance between synthesized and memory positive views. Meanwhile, the synthesized view is pushed away from K negative views in the latent space. Replacing sim(f new , k i ) in Eq. 6, Eq. 7 and Eq. 8 with sim(f, k i ) is another possibility, which pushes away the original view from negative instances. After testing, sim(f new , k i ) works better, because pushing away the synthesized view from negative instances aid the generation of more accurate synthesized views that look different from the K negative instances.

Joint Training

Our proposed GCL framework is trained in a joint training way. Both GAN and contrastive instance discrimination can be trained in a self-supervised manner. While the GAN learns a data distribution via adversarial learning on each instance, contrastive instance discrimination learns representations by retrieving each instance from candidates. In our designed joint training, the two modules work as two collaborators with the same objective: enhancing the quality of representations built by the shared identity encoder E id . We formulate our GCL as an approach to augment contrast for unsupervised ReID. Firstly, the generative module generates online data augmentation, which enhances the positive view diversity for contrastive module. Secondly, the contrastive module, in turn, learns view-invariant representations by matching original and generated views, which refine the generation quality. The joint training boosts both modules simultaneously. Our joint training conducts forward propagation initially on the generative module and subsequently on the contrastive module. Back-propagation is then conducted with an overall loss that combines Eq. 4, Eq. 6, Eq. 7 and Eq. 8.

L all = L gan + L vi + L vi + L vi (9)
To accelerate the training process and alleviate the noise from imperfect generation quality at beginning epochs, we need to warm up the four modules used in the View Generator E id , E str , G and D. We firstly use a state-of-the-art unsupervised ReID method to warm up E id , which is then considered as a baseline in our ablation studies. Generally speaking, any unsupervised ReID method can be used to warm up E id . Before conducting the View Contrast, we freeze E id and warm up E str , G, and D only with GAN loss in Eq. 4 for 40 epochs. In the following, we bring in the memory bank and the pseudo labels to jointly train the whole framework with L all for another 20 epochs. During the joint training, pseudo labels are updated at the beginning of every epoch.

Experiments

Datasets and Evaluation Protocols

Three mainstream person ReID datasets are considered in our experiments, including Market-1501 [START_REF] Zheng | Scalable person re-identification: A benchmark[END_REF], DukeMTMC-reID [START_REF] Ristani | Performance measures and a data set for multi-target, multi-camera tracking[END_REF] and MSMT17 [START_REF] Wei | Person transfer gan to bridge domain gap for person reidentification[END_REF]. Market Following state-of-the-art unsupervised ReID methods [START_REF] Wang | Unsupervised person reidentification via multi-label classification[END_REF]24], we evaluate our proposed method GCL under fully unsupervised setting on the three datasets and under four UDA benchmark protocols, including Market→Duke, Duke→Market, Market→MSMT and Duke→MSMT. We report both quantitative and qualitative results for unsupervised person ReID and view generation.

Implementation Details

We firstly present network design details of E id , E str , G and D. In the following descriptions, we write the size of feature maps in channel×height×width. Our model design is mainly inspired by [START_REF] Zheng | Joint discriminative and generative learning for person re-identification[END_REF][START_REF] Zou | Joint disentangling and adaptation for cross-domain person re-identification[END_REF]. (1) E id is a ImageNet [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF] pre-trained ResNet50 [START_REF] He | Deep residual learning for image recognition[END_REF] with slight modifications. The original fully connected layer is replaced by a fully connected embedding layer, which outputs identity representations f in 512×1×1 for the View Contrast. In parallel, we add a part average pooling that outputs identity features f id in 2048×4×1 for the View Generator. (2) E str is composed of four convolutional and four residual layers, which output structure features f str in 128×64×32. (3) G contains four residual and four convolutional layers. Every residual layer contains two adaptive instance normalization layers [START_REF] Huang | Arbitrary style transfer in real-time with adaptive instance normalization[END_REF] that transform f id into scale and bias parameters. (4) D is a multi-scale PatchGAN [START_REF] Isola | Image-to-image translation with conditional adversarial networks[END_REF] discriminator at 64×32, 128×64 and 256×128.

Then, we present the training and testing configuration details. Our framework is implemented in Pytorch and trained with one Nvidia Titan RTX GPU. [START_REF] Bak | Domain adaptation through synthesis for unsupervised person re-identification[END_REF] For the E id warm-up, we consider JVTC [24], because it is a stateof-the-art ReID method that is compatible with both fully unsupervised and UDA settings. We also test other baselines, e.g., MMCL [START_REF] Wang | Unsupervised person reidentification via multi-label classification[END_REF] and ACT [START_REF] Yang | Asymmetric co-teaching for unsupervised cross-domain person re-identification[END_REF] to demonstrate the generalizability of our method. (2) For training, inputs are resized to 256×128. We empirically set a large weight λ img = λ f eat = 5 for reconstruction in Eq. 4. With a batch size of 16, we use SGD to train E id and Adam optimizer to train E str , G and D. Learning rate is set to 1 × 10 -4 during the warm-up. In the joint-training, learning rate in Adam is set to 1 × 10 -4 and 3.5 × 10 -4 in SGD and are multiplied by 0.1 after 10 epochs. (3) In the View Contrast module, we set the momentum coefficient α = 0.2 in Eq. 5 and the temperature τ = 0.04 in Eq. 6. The number of negatives K is 8192. DBSCAN density radius is set to 2 × 10 -3 . (4) For testing, only E id is conserved and outputs representations f of dimension 512. Important parameters are set by a grid search on the fully unsupervised Market-1501 benchmark. The temperature τ is searched from {0.03, 0.04, 0.05, 0.06, 0.07} and finally is set to 0.04. A smaller τ increases the scale of similarity scores in the Eq. 6, Eq. 7 and Eq. 8, which makes viewinvariant losses more sensitive to inter-instance difference. However, when τ is set to 0.03, these losses become too sensitive and make the training unstable. The number of negatives K is searched from {2048, 4096, 8192}. A larger K pushes away more negatives in the view-invariant losses. Since the Market-1501 dataset has only 12936 training images, we set K = 8192.

Unsupervised ReID Evaluation

Comparison with state-of-the-art methods. Tab. 1 shows the quantitative results on the Market-1501 and DukeMTMC-reID datasets. Tab. 2 shows the quantitative results on the MSMT17 dataset. Our method is mainly designed for fully unsupervised ReID. Under this setting, we test the performance of GCL with three different baselines, including MMCL, JVTC and JVTC+. Our implementation of the three baselines provides results that are slightly different from those mentioned in the corresponding papers. Thus, we firstly report results of our implementations and then add our GCL on these baselines. Our method improves the performance of the baselines by large margins. These improvements show that GANs are not limited to crossdomain style transfer for unsupervised ReID.

Under the UDA setting, we also evaluate the performance of GCL with three different baselines, including ACT, JVTC and JVTC+. The labeled source dataset is only used to warm up our identity encoder E id , but not used in our joint generative and contrastive training. Compared to fully unsupervised methods, the UDA warmed E id is stronger and extracts improved identity features. Thus, the performance of UDA methods is generally higher than fully unsupervised methods. With a strong baseline JVTC+, our GCL achieves state-of-the-art performance.

= E[log (1 + K i=1 exp (sim(f,ki)/τ )
exp (sim(f,fpos)/τ ) )] to train our contrastive module without generation. We also add a set of traditional data augmentation, including random flipping, cropping, jittering, erasing, to train our contrastive module like a traditional memory bank based contrastive method. (3) Joint Generation and Contrast: L vi , L vi and L vi enhance the view-invariance of identity representations between original, synthesized and memory-stored positive views, while negative views are pushed away. We provide how view-invariant representations learned from generated views affect pseudo labels in Appendix C. Table 3: Ablation study on loss functions used in two modules.

( Method FID(realism) SSIM(diversity) Real 7.22 0.350 FD-GAN [START_REF] Ge | Fd-gan: Pose-guided feature distilling gan for robust person re-identification[END_REF] 216.88 0.271 IS-GAN [START_REF] Eom | Learning disentangled representation for robust person re-identification[END_REF] 281.63 0.165 DG-Net [START_REF] Zheng | Joint discriminative and generative learning for person re-identification[END_REF] 18 We also conduct a qualitative ablation study, where synthesized novel views without and with view-invariant losses are illustrated in Fig. 4. Results confirm that E id extracts view-specific identity features (black bag), in the case that view-invariant losses are not used. Given view-invariant losses, E id is able to extract view-invariant identity features (red shirt). Another example is provided in Appendix B.

Generation Quality Evaluation

Comparison with state-of-the-art methods. We compare generated images between our proposed GCL under the JVTC [24] warmed fully unsupervised setting and stateof-the-art GAN-based ReID methods in Fig. 5. FD-GAN [START_REF] Ge | Fd-gan: Pose-guided feature distilling gan for robust person re-identification[END_REF], IS-GAN [START_REF] Eom | Learning disentangled representation for robust person re-identification[END_REF] and DG-Net [START_REF] Zheng | Joint discriminative and generative learning for person re-identification[END_REF] are supervised Re-ID methods. Since the source code of these three methods is available, we compare generated images of same identities. We observe that there exists blur in images generated by FD-GAN and IS-GAN. DG-Net generates sharper images, but different body shapes and some incoherent objects (bags and clothes) are observed. PDA [START_REF] Li | Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation[END_REF] and DG-Net++ [START_REF] Zou | Joint disentangling and adaptation for cross-domain person re-identification[END_REF] are UDA methods, whose source code is not yet released. We can only compare several generated images with unknown identities as illustrated in their papers. PDA generates blurred cross-domain images, whose quality is similar to FD-GAN and IS-GAN. DG-Net++ extends DG-Net into cross-domain generation, which has same problems of body shape and incoherent objects. Our GCL preserves better body shape information and does not generate incoherent objects. Moreover, our GCL is a fully unsupervised method.

We use Fréchet Inception Distance (FID) [START_REF] Heusel | Gans trained by a two time-scale update rule converge to a local nash equilibrium[END_REF] to measure visual quality, as well as Structural SIMilarity (SSIM) [40] to capture structure diversity of generated images. In Tab. 4, we compare our method with FD-GAN [START_REF] Ge | Fd-gan: Pose-guided feature distilling gan for robust person re-identification[END_REF], IS-GAN [START_REF] Eom | Learning disentangled representation for robust person re-identification[END_REF] and DG-Net [START_REF] Zheng | Joint discriminative and generative learning for person re-identification[END_REF], whose source code is available. FID measures the distribution distance between generated and real images, where a lower FID represents the case, where generated images are similar to real ones. SSIM measures the intra-class structural similarity, where a larger SSIM represents a larger diversity. We note that DG-Net is outperforms our method w.r.t. FID, because the distribution is better maintained with ground truth identities in the supervised method DG-Net. However, our method is superior to DG-Net w.r.t. SSIM, as DG-Net swaps intra-dataset structures, whereas our rotated meshes build structures that do not exist in the original dataset. More discussion. To validate, whether identity and structure features can be really disentangled under a fully unsupervised ReID setting, two experiments are conducted by changing firstly only structure features and then only identity features. Results in Fig. 6 show that changing structure features only change structures and do not affect appearances. We also fix structure features and linearly interpolate two random identity feature vectors. Results in Fig. 7 show that identity features only change appearances and do not affect structures in generated images. More examples are provided in Appendix D.

Conclusions

In this paper, we propose a joint generative and contrastive learning framework for unsupervised person ReID. Deviating from previous contrastive methods with traditional data augmentation techniques, we generate diversified views with a 3D mesh guided GAN. These generated novel views are then combined with original images in memory based contrastive learning, in order to learn viewinvariant representations, which in turn improve generation quality. Our generative and contrastive modules mutually promote each other's performance in unsupervised ReID. Moreover, our framework does not rely on a source dataset, which is mandatory in style transfer based methods. Extensive experiments on three datasets validate the effectiveness of our framework in both unsupervised person ReID and multi-view person image generation.

Figure 1 :

 1 Figure 1: Left: Traditional self-supervised contrastive learning maximizes agreement between representations (f1 and f2) of augmented views from Data Augmentation (DA). Right: Joint generative and contrastive learning maximizes agreement between original and generated views.

Figure 2 :

 2 Figure 2: (a) General architecture of GCL: Generative and contrastive modules are coupled by the shared identity encoder E id . (b) Generative module: The decoder G combines the identity features encoded by E id and structure features Estr to generate a novel view x new with a cycle consistency. (c) Contrastive module: View-invariance is enhanced by maximizing the agreement between original E id (x), synthesized E id (x new ) and memory fpos representations.

Figure 3 :

 3 Figure 3: Example images as generated by the View Generator via 3D mesh rotation based on left input image.

  ). Lgan corresponds to generation w/o contrast. (2). L woGAN vi corresponds to contrast w/o generation. TDA denotes traditional data augmentation. (3). Lgan + Lvi (L vi and L vi ) correspond to joint generative and contrastive learning.

Figure 4 :

 4 Figure 4: Qualitative ablation study on the view-invariant losses. For simplicity, Lvi denotes three view-invariant losses Lvi+L vi + L vi , which helps E id to extract view-invariant features (red shirt).

Figure 5 :

 5 Figure 5: Comparison of the generated images on Market-1501 dataset. refers to methods without sharing source code, whose examples are cropped from their papers. Examples of FD-GAN, IS-GAN, DG-Net and GCL are generated from six real images shown in the figure.

Figure 6 :

 6 Figure 6: Generated novel views on the three datasets.

Figure 7 :

 7 Figure 7: Linear interpolation on identity features. Identity features are swapped between left and right persons.

  75.83 62.59 51.22 62.31 70.79 55.00 Duke 58.24 72.21 66.29 57.41 64.61 68.08 55.03 MSMT 54.14 64.46 60.75 55.98 59.78 62.26 48.40 Table 5: FID score on different views.

Figure 10 :

 10 Figure 10: Examples of generated novel views on Market-1501 training and test sets.

Figure 11 :

 11 Figure 11: Examples of generated novel views on DukeMTMC-reID training and test sets.

Figure 12 :

 12 Figure 12: Examples of generated novel views on MSMT17 training and test sets.

Table 1 :

 1 Comparison of unsupervised ReID methods (%) with a ResNet50 backbone on Market and Duke datasets. We test our proposed method on several baselines, whose names are in brackets. * refers to our implementation based on authors' code.

	Method	Reference	Market1501 Source mAP Rank1 Rank5 Rank10 Source mAP Rank1 Rank5 Rank10 DukeMTMC-reID
	BUC [27]	AAAI'19	None	29.6	61.9	73.5	78.2	None	22.1	40.4	52.5	58.2
	SoftSim [28]	CVPR'20	None	37.8	71.7	83.8	87.4	None	28.6	52.5	63.5	68.9
	TSSL [42]	AAAI'20	None	43.3	71.2	-	-	None	38.5	62.2	-	-
	MMCL [35]	CVPR'20	None	45.5	80.3	89.4	92.3	None	40.2	65.2	75.9	80.0
	JVTC [24]	ECCV'20	None	41.8	72.9	84.2	88.7	None	42.2	67.6	78.0	81.6
	JVTC+ [24]	ECCV'20	None	47.5	79.5	89.2	91.9	None	50.7	74.6	82.9	85.3
	MMCL*	This paper	None	45.1	79.5	89.0	91.9	None	40.9	64.8	75.2	79.8
	JVTC*	This paper	None	47.2	75.4	86.7	90.5	None	43.9	66.8	77.6	81.0
	JVTC+*	This paper	None	50.9	79.1	89.8	92.9	None	52.8	74.9	83.3	85.8
	ours(MMCL*)	This paper	None	54.9	83.7	91.6	94.0	None	49.3	69.7	79.7	82.8
	ours(JVTC*)	This paper	None	63.4	83.7	91.6	94.3	None	53.3	72.4	82.0	84.9
	ours(JVTC+*)	This paper	None	66.8	87.3	93.5	95.5	None	62.8	82.9	87.1	88.5
	ECN [51]	CVPR'19	Duke	43.0	75.1	87.6	91.6	Market 40.4	63.3	75.8	80.4
	PDA [25]	ICCV'19	Duke	47.6	75.2	86.3	90.2	Market 45.1	63.2	77.0	82.5
	CR-GAN [8]	ICCV'19	Duke	54.0	77.7	89.7	92.7	Market 48.6	68.9	80.2	84.7
	SSG [12]	ICCV'19	Duke	58.3	80.0	90.0	92.4	Market 53.4	73.0	80.6	83.2
	MMCL [35]	CVPR'20	Duke	60.4	84.4	92.8	95.0	Market 51.4	72.4	82.9	85.0
	ACT [45]	AAAI'20	Duke	60.6	80.5	-	-	Market 54.5	72.4	-	-
	DG-Net++ [55] ECCV'20	Duke	61.7	82.1	90.2	92.7	Market 63.8	78.9	87.8	90.4
	JVTC [24]	ECCV'20	Duke	61.1	83.8	93.0	95.2	Market 56.2	75.0	85.1	88.2
	ECN+ [52]	PAMI'20	Duke	63.8	84.1	92.8	95.4	Market 54.4	74.0	83.7	87.4
	JVTC+ [24]	ECCV'20	Duke	67.2	86.8	95.2	97.1	Market 66.5	80.4	89.9	92.2
	MMT [13]	ICLR'20	Duke	71.2	87.7	94.9	96.9	Market 65.1	78.0	88.8	92.5
	CAIL [29]	ECCV'20	Duke	71.5	88.1	94.4	96.2	Market 65.2	79.5	88.3	91.4
	ACT*	This paper	Duke	59.1	78.8	88.9	91.7	Market 51.5	70.9	80.0	83.4
	JVTC*	This paper	Duke	65.0	85.7	93.6	95.6	Market 56.5	73.9	84.5	87.7
	JVTC+*	This paper	Duke	67.6	87.0	95.2	97.0	Market 66.7	81.0	89.9	91.5
	ours(ACT*)	This paper	Duke	66.7	83.9	91.4	93.4	Market 55.4	71.9	81.6	84.6
	ours(JVTC*)	This paper	Duke	73.4	89.1	95.0	96.6	Market 60.4	77.2	86.2	88.4
	ours(JVTC+*)	This paper	Duke	75.4	90.5	96.2	97.1	Market 67.6	81.9	88.9	90.6

Table 2 :

 2 Comparison of unsupervised Re-ID methods (%) with a ResNet50 backbone on MSMT17. * refers to our implementation based on authors' code.

	Method	Reference	MSMT17 Source mAP R1	R5	R10
	MMCL [35]	CVPR'20	None	11.2 35.4 44.8 49.8
	JVTC [24]	ECCV'20	None	15.1 39.0 50.9 56.8
	JVTC+ [24]	ECCV'20	None	17.3 43.1 53.8 59.4
	JVTC*	This paper	None	13.4 36.0 48.8 54.9
	JVTC+*	This paper	None	16.3 40.4 55.6 61.8
	ours(JVTC*)	This paper	None	18.0 41.6 53.2 58.4
	ours(JVTC+*)	This paper	None	21.3 45.7 58.6 64.5
	ECN [51]	CVPR'19 Market	8.5	25.3 36.3 42.1
	SSG [12]	ICCV'19 Market 13.2 31.6 49.6	-
	MMCL [35]	CVPR'20 Market 15.1 40.8 51.8 56.7
	ECN+ [52]	PAMI'20 Market 15.2 40.4 53.1 58.7
	JVTC [24]	ECCV'20 Market 19.0 42.1 53.4 58.9
	DG-Net++ [55] ECCV'20 Market 22.1 48.4 60.9 66.1
	CAIL [29]	ECCV'20 Market 20.4 43.7 56.1 61.9
	MMT [13]	ICLR'20	Market 22.9 49.2 63.1 68.8
	JVTC+ [24]	ECCV'20 Market 25.1 48.6 65.3 68.2
	JVTC*	This paper Market 17.1 39.6 53.3 59.3
	JVTC+*	This paper Market 20.5 44.0 59.5 71.1
	ours(JVTC*)	This paper Market 21.5 45.0 57.1 66.5
	ours(JVTC+*)	This paper Market 27.0 51.1 63.9 69.9
	ECN [51]	CVPR'19	Duke	10.2 30.2 41.5 46.8
	SSG [12]	ICCV'19	Duke	13.3 32.2 51.2	-
	MMCL [35]	CVPR'20	Duke	16.2 43.6 54.3 58.9
	ECN+ [52]	PAMI'20	Duke	16.0 42.5 55.9 61.5
	JVTC [24]	ECCV'20	Duke	20.3 45.4 58.4 64.3
	DG-Net++ [55] ECCV'20	Duke	22.1 48.8 60.9 65.9
	MMT [13]	ICLR'20	Duke	23.3 50.1 63.9 69.8
	CAIL [29]	ECCV'20	Duke	24.3 51.7 64.0 68.9
	JVTC+ [24]	ECCV'20	Duke	27.5 52.9 70.5 75.9
	JVTC*	This paper	Duke	19.9 45.4 59.1 64.9
	JVTC+*	This paper	Duke	23.6 49.4 65.2 71.1
	ours(JVTC*)	This paper	Duke	24.9 50.8 63.4 68.9
	ours(JVTC+*)	This paper	Duke	29.7 54.4 68.2 74.2
	Ablation Study. To better understand the contribution of
	generative and contrastive modules, we conduct ablation
	experiments on the two fully unsupervised benchmarks:
	Market-1501 and DukeMTMC-reID. Quantitative results
	with a JVTC baseline are reported in Tab. 3. By gradually
	adding loss functions on the baseline, our ablation experi-
	ments correspond to three scenarios. (1) Only Generation:
	with only L gan , our generation module disentangles iden-
	tity and structure features. Since there is no inter-view con-
	straint, E id tends to extract view-specific identity features,
	which decreases the ReID performance. (2) Only Contrast:
	we use L woGAN vi

  +L gan + L vi + L vi + L vi 63.4

	Loss	Market-1501 mAP Rank1 mAP DukeMTMC-reID Rank1
	Baseline	47.2	75.4	43.9	66.8
	+L gan	41.6	69.0	25.8	45.9
	+L woGAN vi +L woGAN vi + T DA	47.8 53.7	75.2 78.7	44.1 48.5	67.8 70.0
	+L gan + L vi	54.1	79.4	47.4	68.4
	+L gan + L vi + L vi	59.2	82.2	50.5	71.0
			83.7	53.3	72.4

Table 4 :

 4 Comparison of FID (lower is better) and SSIM (higher is better) on Market-1501 dataset. U denotes the fully unsupervised setting. UDA denotes Duke→Market setting.

		.24	0.360
	Ours(U)	59.86	0.367
	Ours(UDA)	53.07	0.369

Acknowledgements

This work has been supported by the French government, through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. The authors are grateful to the OPAL infrastructure from Université Côte d'Azur for providing resources and support.

Source code and models are available under https: //github

Appendices Appendix A. Cycle consistency

Two kinds of cycle consistency are used in our proposed method GCL. 1) Based on a popular assumption in ReID that two images of a same identity should have same nearest neighbors in the dataset, we have calculated k-reciprocal Jaccard distance [START_REF] Zhong | Reranking person re-identification with k-reciprocal encoding[END_REF] for the DBSCAN clustering. This operation effectively makes pseudo labels more reliable for contrastive learning. 2) Since no paired data are available, we have used the CycleGAN [START_REF] Zhu | Unpaired image-to-image translation using cycleconsistent adversarial networks[END_REF] structure to supervise the generative module. By minimizing the image and feature reconstruction losses in a cycle consistency, representations are disentangled into appearance and structure features, which permits generating person images in novel view-points without changing identity information.

Appendix B. View-invariant losses

We illustrate another example in Fig. 8 to confirm the effectiveness of the view-invariant losses in generation. When GCL is trained without the view-invariant losses, GCL degrades to a traditional CycleGAN, which is prone to affected by the noise inside the original image. The viewinvariant losses help the identity encoder to extract identity features shared between different views, which are robust to the noise inside the original image.

Appendix C. Effects on pseudo labels

We minimize intra-class variance via contrasting generated images, which leads to a larger inter-class distance in latent space. Learning view-invariant representations from diversified generated data helps clustering algorithms to generate more accurate pseudo labels. With a same DB-SCAN clustering, the cluster number of GCL is closer to real identity number than that of contrastive learning with traditional data augmentation. For example, Market-1501 dataset has 751 real identities. DBSCAN in GCL categorizes unlabeled images into around 520 clusters, while the contrastive learning with traditional data augmentation has around 460 clusters (see Fig. 9).

Appendix D. Generated views

We illustrate more examples of generated views with a JVTC [24] fully unuspervised baseline on Market-1501 in Fig. 10, DukeMTCM-reID in Fig. 11 and MSMT17 in Fig. 12. Here, we show generated examples from both training and test sets to confirm the effectiveness of our GCL. Generally, the generation quality is good enough to help our GCL learn view-invariant representations. However, there are still some limitations, e.g., some visual blurs still exist and detailed identity information is lost in some cases (in the bottom row of Fig. 10, the red logo on the shorts disappears in the generated images). In future work, we believe that the visual blurs can be alleviated by leveraging the architectures from more recent GANs [2, 23] in our generator and detailed identity information can be better preserved when better unsupervised baselines are available.

Generally, it is easier to generate novel views of 45°, 180°and 315°. 45°and 315°are small rotations, in which original and synthesized images can share maximal identity information. 180°can be roughly regarded as a horizontal flipping. Results in Tab. 5 verify this supposition. Our generated novel views on the three datasets will be released as a new dataset to facilitate future research on view-invariant and unsupervised ReID.