Datasets:
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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
[](https://arxiv.org/abs/2605.23699)
[](https://genintel.github.io/CRONOS/)
[](https://huggingface.co/datasets/genintel/CRONOS-benchmark)
[](https://github.com/GenIntel/CRONOS-benchmark)
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 and a `metadata.json` file, 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 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)
└── metadata.json # sequence metadata and text prompt
```
### 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 |
| `metadata.json` | JSON | — | Sequence identifiers (`event`, `scene`, `object`, `appearance`, `view`) and a natural-language `prompt` describing the expected physical event |
### 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
```bibtex
@misc{begiristain2026cronos,
title={CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models},
author={Le{\'o}n Begiristain and Olaf D{\"u}nkel and Adam Kortylewski},
year={2026},
eprint={2605.23699},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.23699},
}
``` |