YouZhe/YoCausal-dataset

YoCausal: A Causality Benchmark for Video Generation Models

You-Zhe Xie^🦊🌐*, Yu-Hsuan Li^🦊*, Jie-Ying Lee^🦊, Kaipeng Zhang^🌐,
Yu-Lun Liu^🦊†, Zhixiang Wang^🌐†

🦊 National Yang Ming Chiao Tung University    🌐 Shanda AI Research Tokyo
^* Equal contribution    ^† Corresponding authors

Overview

YoCausal (ようこそ, Yōkoso) evaluates whether video generation models are sensitive to causal temporal structure. Each natural video is paired with its time-reversed counterpart; models are compared on forward versus backward denoising loss using the same prompt and noise condition.

YoCausal accompanies YoCausal: How Far is Video Generation from World Model? A Causality Perspective and is intended for benchmark evaluation rather than training.

• Project page: https://www.youzhexie.me/papers/YoCausal/index.html
• Huggingface paper page: https://huggingface.co/papers/2605.30346
• Arxiv Paper page: http://arxiv.org/abs/2605.30346
• Dataset repository: https://huggingface.co/datasets/YouZhe/YoCausal-dataset
• Code repository: https://github.com/youzhe0305/YoCausal
• Contents: 1,232 forward-backward pairs (2,464 MP4 files) with English prompts and JSON metadata

Dataset Structure

Subset	Source collection	Pairs	Duration	Content
general	Moments in Time	500	3 s	Everyday events and actions
physics	Physics-IQ	132	5 s	Physical phenomena
human	Kinetics-400 subset	400	3 s	Human actions
animal	Animal Kingdom	200	3 s	Animal behavior
Total		1,232

subset/<name>/
  dataset_metadata.json
  fwd/*.mp4
  bwd/*.mp4

Each metadata record refers to one pair. fwd/ contains natural videos and bwd/ contains the corresponding temporally reversed videos.

Metadata

Each dataset_metadata.json is a JSON array with the following fields:

Field	Description
id	Entry identifier within a subset.
video_path_forward, video_path_backward	Preparation-time video paths; use their basenames to locate released MP4 files.
prompt	Caption used for both temporal directions during evaluation.
dataset_source, category	Source collection and content category.
meta	Recorded fps, [height, width] resolution, and total_frames.
vlm_causality	VLM-based binary label for observable causal structure.
human_discriminable	Human temporal-direction judgment: true, false, or "unknown"(see paper for details).

Loading

Videos are stored with Git LFS:

git lfs install
git clone https://huggingface.co/datasets/YouZhe/YoCausal-dataset

The metadata preserves preparation-time paths. Resolve released videos by filename:

import json
from pathlib import Path

root = Path("YoCausal-dataset")
subset = "animal"
with (root / "subset" / subset / "dataset_metadata.json").open() as f:
    sample = json.load(f)[0]

name = Path(sample["video_path_forward"]).name
fwd = root / "subset" / subset / "fwd" / name
bwd = root / "subset" / subset / "bwd" / name

Evaluation Protocol

For each pair, the model receives the same prompt and matched noise for both directions. A forward win occurs when the loss on the natural video is lower than on its reversed counterpart.

Metric	Meaning
RSI	Forward win rate, measuring temporal-direction sensitivity.
RSI(Dc) / RSI(Dnc)	RSI on vlm_causality=true / false examples.
CCI	RSI(Dc) - RSI(Dnc), intended to separate causal sensitivity from general temporal preference.

When evaluating human-discriminable versus non-discriminable subsets, entries with human_discriminable="unknown" should be retained as unlabeled for that split and excluded from the true / false subgroup comparison.

Citation

@article{xie2026yocausal,
  title   = {YoCausal: How Far is Video Generation from World Model? A Causality Perspective},
  author  = {Xie, You-Zhe and Li, Yu-Hsuan and Lee, Jie-Ying and Zhang, Kaipeng and Liu, Yu-Lun and Wang, Zhixiang},
  journal = {arXiv preprint arXiv:2605.30346},
  year    = {2026}
}