full-action animated WebPs (hero + 4 per-scenario), trim heterogeneity prose

README.md

@@ -26,7 +26,7 @@ tags:
 
 # PhysicalAI SDG-Warehouse
 
-![Hero — four warehouse-safety scenarios in synchronized multi-view simulation.](./assets/hero_2x2.gif)
+![Hero — four warehouse-safety scenarios in synchronized multi-view simulation.](./assets/hero_2x2.webp)
 
 PhysicalAI SDG-Warehouse is a synthetic, fully-annotated video dataset of staged industrial-safety events captured in a simulated warehouse environment. It contains approximately 123 thousand video clips, totaling roughly 412 hours of footage at 1920 by 1080 resolution and 30 frames per second, organized across four scenarios: a forklift near-miss with a human worker, a warehouse fire with worker evacuation, a forklift collision with a populated storage shelf, and a routine box-pickup action. Every multi-camera simulation run is filmed from five to ten synchronized viewpoints, and the entire pipeline is reproducible end-to-end from a single random seed.
 
@@ -78,18 +78,26 @@ Each scenario stages a different self-contained event inside a shared warehouse
 
 A worker stands at a fixed location while a forklift navigates along a planned path toward the same location. A configurable last-moment dodge distance distinguishes a near-miss from a direct-contact event, so the same scene composition can produce both event classes by varying a single parameter. Each multi-camera run is captured by a mixture of ceiling-mounted CCTV-style cameras (camera aliases `ceiling_00` through `ceiling_04`) and worker-height eye-level cameras (`eye_00` through `eye_04`).
 
+![Forklift–human near-miss — full 10-second run from a ceiling camera.](./assets/clip_nearmiss.webp)
+
 ### Warehouse fire
 
 A localized volumetric fire ignites at a randomized position and time while a small crew of workers performs random walks. On ignition, each worker reacts: it orients toward the flame and then runs toward a designated exit waypoint along a navigation-mesh path. The result is rare emergency-response footage that combines dynamic flames, smoke, and coordinated human evacuation in a single shot. Cameras are placed at ceiling height to maximize floor coverage, with aliases `ceiling_00` through `ceiling_04`.
 
+![Warehouse fire — full 10-second run, ignition followed by coordinated evacuation.](./assets/clip_fire.webp)
+
 ### Forklift–shelf collision
 
 A forklift drives at a parameterized initial distance toward a populated storage shelf and impacts it, producing visible rigid-body knock-over and debris dynamics. An optional character can be placed along the forklift's path to extend the scenario to a three-body forklift–shelf–human event. Cameras are placed circularly around the impact site at varying heights, distances, and look-down angles, with aliases `cam_00` through `cam_05`.
 
+![Forklift–shelf collision — full 15-second run, drive into the shelf and ensuing debris.](./assets/clip_forklift_collision.webp)
+
 ### Warehouse box pickup
 
 A worker navigates to a randomly placed box, performs a contact-rich pickup motion, and carries the box through the warehouse. This scenario provides routine, non-incident action coverage as a counterpoint to the three safety scenarios. The camera rig is a mixed CCTV and eye-level configuration, with aliases `cam_00` through `cam_09`.
 
+![Warehouse box pickup — full 15-second run, walk plus contact-rich pickup plus carry.](./assets/clip_box_pickup.webp)
+
 ## Multi-view coverage
 
 Every multi-camera simulation run is captured from five to ten synchronized cameras. For the near-miss scenario, the rig consists of five ceiling-mounted CCTV-style cameras and five worker-height eye-level cameras, all pointed at the interaction. The figure below shows a single near-miss run from each of the ten viewpoints; because all cameras share a clock and the same scene, the same instant in time appears across all ten frames.
@@ -112,15 +120,13 @@ The current RGB tier publishes the photoreal RGB video and the run-level metadat
 
 | Scenario | Number of clips | Number of runs (WebDataset samples) | Clip length | Cameras per run | Repository path |
 |---|---:|---:|---:|---:|---|
-| Forklift–human near-miss | 27,939 | 13,410 | 10 seconds | 10 or 1 | `rgb/forklift_human_nearmiss/` |
+| Forklift–human near-miss | 27,939 | 13,410 | 10 seconds | 10 | `rgb/forklift_human_nearmiss/` |
 | Warehouse fire | 44,734 | 9,064 | 10 seconds | 5 | `rgb/warehouse_fire/` |
 | Forklift–shelf collision | 24,617 | 4,120 | 15 seconds | 6 | `rgb/forklift_shelf_collision/` |
 | Warehouse box pickup | 25,677 | 2,601 | 15 seconds | 10 | `rgb/warehouse_box_pickup/` |
 | **Total** | **122,967** | **29,195** | — | — | — |
 
-The aggregate footage is approximately 412 hours at 1920 by 1080 resolution and 30 frames per second. The "Number of runs" column corresponds to distinct WebDataset samples, that is, the number of `__key__` values you will observe when iterating with a WebDataset reader.
-
-The near-miss row is heterogeneous because it merges two source pipelines. The first is 1,642 multi-camera runs from the Isaac Replicator Object plus Isaac Replicator Agent pipeline, each captured from ten synchronized cameras, contributing 16,171 clips. The second is 11,768 single-camera clips that were generated by an earlier pipeline and that we include here for completeness, packed as one-camera samples with no per-run scene-composition metadata. The two sources are distinguished by the `source_kind` field in each sample's `meta.json` and in `metadata/clips.parquet`, with the values `multi_camera_run` and `extras_single_view` respectively. The single-camera near-miss samples are located in the shard range `nearmiss-rgb-00051.tar` through `nearmiss-rgb-00112.tar`.
+The aggregate footage is approximately 412 hours at 1920 by 1080 resolution and 30 frames per second. The "Number of runs" column corresponds to distinct WebDataset samples, that is, the number of `__key__` values you will observe when iterating with a WebDataset reader. A small portion of the near-miss split (single-camera samples carried over from an earlier pipeline) is flagged in each sample's `meta.json` via a `source_kind` field; both kinds otherwise share the same schema and can be used together or filtered.
 
 ## Simulation pipeline
 
@@ -262,8 +268,6 @@ local_tar = hf_hub_download(
 
 This release publishes the RGB tier only. The full annotation stack — metric depth, instance and shaded segmentation, Canny edges, two-dimensional and three-dimensional bounding boxes, and per-frame camera intrinsics and extrinsics — is already rendered, aligned to every RGB frame, and will be added to this same repository as a companion `artifacts/` tier. The visualizations in the ground-truth modalities figure above were produced from those native renders.
 
-The near-miss scenario is heterogeneous in composition. It combines 1,642 multi-camera runs (ten cameras each) with 11,768 single-camera legacy clips from older training-set dumps; the latter do not carry run-level scene-composition metadata and are located in the second half of the near-miss shard range. The `source_kind` field on every sample disambiguates the two sources.
-
 The dataset is fully synthetic and exhibits a sim-to-real gap. Compared to real warehouse footage, the rendered material can have a computer-graphics-like appearance, simplified material response, and limited fidelity in volumetric effects such as smoke and fire. Models trained on the dataset should be carefully evaluated on real footage before being deployed in any safety-critical setting.
 
 Agent motion is driven by procedural navigation and behavior, so reactions — and in particular the coordinated evacuation behavior in the fire scenario — can occasionally appear unnatural. Rigid-body interactions between the forklift and the storage shelves are physically simulated, but very fine-grained debris, deformation, and secondary contact effects are approximate.