Datasets:

genintel
/

CRONOS-benchmark

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 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).

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

@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}, 
}