---
license: cc-by-nc-4.0
pretty_name: ECCV'26 PhysAI Challenge — NVS Syn4D subset
task_categories:
  - image-to-video
tags:
  - novel-view-synthesis
  - camera-control
  - video-diffusion
  - syn4d
  - eccv2026
size_categories:
  - n<1K
---

# ECCV'26 PhysAI Challenge — Novel-View Synthesis (Syn4D subset)

A small, **self-contained** novel-view-synthesis (NVS) evaluation package for the
ECCV'26 PhysAI workshop challenge (video-diffusion NVS under camera control). It ships
the held-out Syn4D inputs (**source rgb + depth + camera + segmentation mask**) and the
matching **RecamMaster raw-resolution predictions**, so you can reproduce the reference
metrics end-to-end with no dataset access and no inference.

> ## 👉 The evaluation CODE lives here, not on this dataset page
> **Scorer / benchmark: https://github.com/NIRVANALAN/eccv26-physai-workshop-vdm_nvs_bench**
> (`vdm-nvs-bench`) — install it, then score any submission (DAVIS or Syn4D track).
> This HF dataset is only the **data package** (inputs + a reference model's outputs).

---

## What's inside

One file: **`nvs_syn4d_eval_set_recammaster.tar.zst`** (~311 MB). It expands to:

```
nvs_syn4d_eval_set/
  official/<pair_id>/          # 7 eval pairs — flying_group/seq_000001, src view 0 -> tgt views 1..7
      source.mp4 target.mp4         # RGB 81x480x832  (target = novel-view GT)
      cameras.npz                   # source/target/rel_target c2w (81,4,4) + K + frame_ids
      {source,target}_depth.npz     # z-depth (81,480,832) f16
      {source,target}_mask.npz      # dynamic-fg mask (¬env)
      meta.json + previews
  recammaster_out/<pair_id>/   # RecamMaster (original step20000) RAW predictions, 832x480x81
      pred_*.mp4 gt_*.mp4 source_*.mp4 compare_*.mp4
  tools/ run_recammaster_1gpu.sh docs/  score_out_reference/   # scorer adapter, runner, docs, expected metrics
  README.md  REPRODUCE.md
```

`flying_group` is one of two never-trained held-out Syn4D scenes. **A→B semantics:**
source view 0 is the input; the model renders each target view's camera trajectory;
`target.mp4` is the real rendered target = paired GT.

## Public iPhone validation bundle

**`iphone_nvs_validation_bundle.tar.gz`** is a separate, public local-validation
package for the workshop's NVS track.  It contains five fully paired iPhone
source-to-target clips (`apple`, `block`, `paper-windmill`, `spin`, `teddy`),
each at 49 frames and 288×512, together with source LiDAR depth, refined source
and target camera trajectories, and target evaluation masks.  The target RGB is
intentionally public for local validation; it is not part of the hidden Syn4D
Kaggle test set.

```bash
tar -xzf iphone_nvs_validation_bundle.tar.gz
```

Use the workshop repository's
[`iPhone validation protocol`](https://github.com/NIRVANALAN/eccv26-physai-workshop-vdm_nvs_bench/blob/main/docs/iphone_nvs_validation.md)
for the prediction layout and evaluation command.

## Quick start

```bash
# 1) download this dataset and extract:
hf download yslan/ECCV26_PhysAI_Challenge_NVS_Syn4D_subset --repo-type=dataset --local-dir nvs_syn4d_subset
tar --use-compress-program=unzstd -xf nvs_syn4d_subset/nvs_syn4d_eval_set_recammaster.tar.zst
cd nvs_syn4d_eval_set

# 2) set up the scorer ONCE (see the GitHub repo's "Setup"): a python env, the OFFICIAL
#    vggt-omega, and the bench itself:
#      conda create -n vdm-nvs-bench python=3.10 -y && conda activate vdm-nvs-bench
#      git clone https://github.com/facebookresearch/vggt-omega && pip install -e vggt-omega
#      git clone https://github.com/NIRVANALAN/eccv26-physai-workshop-vdm_nvs_bench && \
#        pip install -e eccv26-physai-workshop-vdm_nvs_bench && \
#        python eccv26-physai-workshop-vdm_nvs_bench/scripts/download_weights.py

# 3) score (the bench is on PATH after pip install; else set VDM_NVS_BENCH=/path/to/it):
python tools/score_recammaster.py --out score_out
python tools/final_row.py
```
Full walkthrough (prereqs, re-inference, scoring your own model): `REPRODUCE.md` inside the archive.

## Reference metrics (what you'll reproduce)

RecamMaster (original ReCamMaster step20000, raw 832×480×81), 7 pairs, canonical
**ViT-H-14** CLIP, native 81 frames:

| n | ATE ↓ | trans ↓ | rot° ↓ | CLIP-V ↑ | CLIP-F ↑ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| 7 | 0.0478 | 0.0224 | 0.434 | 0.862 | 0.969 | 12.61 | 0.227 | 0.573 |

Camera = VGGT-Omega vs the requested trajectory; appearance = paired vs GT.
**FVD is omitted** — 7 clips is far too small for a stable Fréchet estimate (it is
still computed into `score_out/video_metrics.json`, just not headlined).

## Reproduce the predictions from the official RecamMaster repo

The shipped `recammaster_out/` predictions were generated by the **original ReCamMaster
step20000** checkpoint at native **832×480×81**. To regenerate them yourself:

**Prereqs:** the `recammaster-official` repo (provides the `diffsynth`
`WanVideoReCamMasterPipeline`), the original ReCamMaster `step20000.ckpt` + the
`Wan2.1-T2V-1.3B` base, the Syn4D dataset, and its conda env (`sync4d`).

**Option A — the official repo's own eval script** (authentic RecamMaster baseline):
```bash
cd /path/to/recammaster-official
GPUS=0,1 SCENE=flying_group SEQ_ROOT_LIST=seq_000001 \
  SOURCE_VIEW=0 TARGET_VIEWS=1,2,3,4,5,6,7 \
  CKPT_PATH=/path/to/ReCamMaster/checkpoints/step20000.ckpt \
  DATASET_ROOT=/path/to/Syn4D \
  bash bash_scripts/eval-metrics/eval-syn4d-flyinggroup-recammaster-step20000-2gpu.sh
```
This samples all 7 targets at native 832×480×81, 81 frames (`inference_recammaster_syn4d.py`),
splitting the targets across 2 GPUs. Outputs land under
`eval/syn4d-nvs-syn4dEval/multimodal_flying_group_seq_000001/recammaster_official_step20000_.../flying_group/seq_000001/`
as `pred_native_rgb_src0_to_tgt{1..7}_chunk0-80.mp4` (+ `gt_native_rgb_*`, `source_native_view0_*`).

**Option B — the bundled 1-GPU runner** (identical inference code, drops straight into the
scorer layout — this is what produced the shipped `recammaster_out/`):
```bash
cd nvs_syn4d_eval_set
export RECAM_REPO=/path/to/recammaster-official      # provides diffsynth
export CKPT_PATH=/path/to/ReCamMaster/checkpoints/step20000.ckpt
export DATASET_ROOT=/path/to/Syn4D
GPU=0 N=7 FORCE_RERUN=1 bash run_recammaster_1gpu.sh   # writes recammaster_out/<pair>/…/pred_*.mp4
```
The RecamMaster inference entry script is **vendored** in the archive under
`tools/recammaster/` (only the heavy `diffsynth` pipeline comes from `RECAM_REPO`).
Inference is deterministic (fixed seed=0) → the same ckpt reproduces the same frames.
Then score as in **Quick start**.

## License / attribution

Derived from the Syn4D dataset; released for the ECCV'26 PhysAI challenge under
CC-BY-NC-4.0. Predictions are from the original ReCamMaster model. Please cite the
challenge and the respective source works.