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README.md

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 ---
 license: apache-2.0
+task_categories:
+  - video-to-video
+  - image-to-video
+tags:
+  - benchmark
+  - physics
+  - causal-reasoning
+  - video-generation
+  - world-models
+size_categories:
+  - 10G<n<100G
 ---
+
+# CRONOS-Benchmark dataset
+
+CRONOS-Benchmark is a controlled, synthetic benchmark for evaluating **counterfactual physical consistency** in video world models. It tests whether generative video models correctly simulate three fundamental physical event types — **object falling**, **object collision**, and **object occlusion** — across diverse scenes, objects, and viewpoints.
+
+Each sequence provides ground-truth RGB frames, depth maps, and segmentation masks, along with a 5-frame conditioning clip, enabling standardised evaluation of any video generation model that accepts image or video conditioning.
+
+## Dataset Details
+
+### Dataset Description
+
+CRONOS-Benchmark dataset contains **675 sequences** rendered in a controlled simulation environment. Each sequence captures one of three physical event types with foreground objects placed in a realistic indoor or outdoor scene. Ground-truth multi-modal annotations (RGB, depth, mask) are provided for all 90 frames of every sequence.
+
+- **License:** Apache 2.0
+
+### Dataset Sources
+
+- **Repository:** TBD
+- **Paper:** TBD
+
+## Dataset Structure
+
+### Taxonomy
+
+| Axis | Values |
+|------|--------|
+| **Event type** | `fall`, `collision`, `occlusion` |
+| **Scene** | `apartment`, `house`, `garden`, `pool`, `kitchen` |
+| **Object** | `Bottle`, `Can`, `TennisBall`, `ToyTruck`, `SoccerBall` |
+| **Appearance** | `appearance_0`, `appearance_1`, `appearance_2` |
+| **Viewpoint** | `close`, `front`, `side`, `top` (fall/collision); `side` only (occlusion) |
+
+### Sequence counts
+
+| Event type | Sequences |
+|------------|-----------|
+| fall       | 300       |
+| collision  | 300       |
+| occlusion  | 75        |
+| **Total**  | **675**   |
+
+### Directory layout
+
+```
+dataset/
+└── {event}/                          # fall | collision | occlusion
+    └── {scene}/                      # apartment | house | garden | pool | kitchen
+        └── {object}/                 # Bottle | Can | TennisBall | ToyTruck | SoccerBall
+            └── {appearance}/         # appearance_0 | appearance_1 | appearance_2
+                └── {viewpoint}/      # close | front | side | top
+                    ├── rgb/          # 90 × frame_XXXX.jpg  (1920×1080, colour)
+                    ├── depth/        # 90 × frame_XXXX.jpg  (1920×1080, depth)
+                    ├── mask/         # 90 × frame_XXXX.jpg  (1920×1080, binary)
+                    └── movies/
+                        ├── complete.mp4        # full 90-frame sequence
+                        └── conditioning_5.mp4  # first 5 frames (conditioning input)
+```
+
+### Modalities
+
+| Modality | Format | Resolution | Description |
+|----------|--------|------------|-------------|
+| RGB | JPEG | 1920 × 1080 | Colour render of the scene |
+| Depth | JPEG | 1920 × 1080 | Per-frame depth map |
+| Mask | JPEG | 1920 × 1080 | Per object segmentation mask |
+| `complete.mp4` | MP4 | 1920 × 1080 | Full ground-truth sequence (90 frames) |
+| `conditioning_5.mp4` | MP4 | 1920 × 1080 | First 5 frames, used as model conditioning input |
+
+### Dataset statistics
+
+| Stat | Value |
+|------|-------|
+| Total sequences | 675 |
+| Frames per sequence | 90 |
+| Total JPEG frames | 182,250 |
+| Total MP4 files | 1,350 |
+| Uncompressed size | ~18.8 GB |
+
+
+
+## Uses
+
+### Direct Use
+
+CRONOS-Benchmark is designed to evaluate video generation / world models on physical event generation. Given the first frame or a conditioning clip as input, a model is asked to generate the remaining frames of the sequence. 
+
+
+### Out-of-Scope Use
+
+- Real-world scene understanding (data is fully synthetic).
+
+## Dataset Creation
+
+
+### Source Data
+
+#### Data Collection and Processing
+
+All sequences were rendered synthetically using UnrealEngine5. All 3D assets were bought from the [Unreal Engine asset store (FAB)](https://fab.com).
+
+#### Personal and Sensitive Information
+
+The dataset contains no personal or sensitive information. All content is synthetic.
+
+## Bias, Risks, and Limitations
+
+- **Synthetic domain gap:** Models trained or tuned on real video may underperform relative to their true capability due to the synthetic appearance of the scenes.
+- **Limited scene and object set:** Our combinatorial design limits the number of variations we can generate per visual factor. Therefore, only five scenes, five object and four views are included.
+
+
+
+## Citation
+
+TBD
+

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