Datasets:

KlingTeam
/

UniBench

pretty_name: UniBench
license: apache-2.0
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - video-generation
  - benchmark
  - depth-estimation
  - optical-flow
  - multimodal
  - world-aware
arxiv: '2512.07831'
configs:
  - config_name: uedata
    data_files:
      - split: train
        path: UniBench/UEData/train/train.csv
      - split: validation
        path: UniBench/UEData/eval/eval.csv
  - config_name: realdata
    data_files:
      - split: test
        path: UniBench/RealData/eval.csv

UniBench

The official evaluation benchmark for UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation.

UniBench evaluates world-aware video generation and estimation across RGB video, optical flow, and depth. This repository contains the benchmark data and metadata used by the paper; it is not the large-scale training-data release.

UniBench vs. OpenUni

Repository	Purpose	Contents
KlingTeam/UniBench	Evaluation benchmark reported in the UnityVideo paper	UEData RGB/RAFT/depth triplets and a public real-video evaluation subset
JackAILab/OpenUni	Large-scale training data for UnityVideo	The OpenUni training corpus and its multimodal annotations

Use OpenUni to train or fine-tune models and UniBench to run the paper's benchmark evaluation. Keeping the two repositories separate avoids mixing training samples with benchmark splits.

Dataset Contents

Subset	Split	Cases	Modalities	Storage
UEData	`train` / reference	29,800	RGB, RAFT optical flow, depth	100 uncompressed tar shards
UEData	`eval`	200	RGB, RAFT optical flow, depth	1 uncompressed tar shard
RealData	`eval`	100	RGB video	Directly browsable MP4 files

UEData contains 30,000 cases in total. Each UEData row points to three aligned videos: the source RGB video (ceph_path), optical flow visualization (raft), and depth visualization (depth). RealData contains 100 public Koala36M samples and does not provide RAFT or depth files.

UniBench/
├── manifest.json
├── UEData/
│   ├── train/
│   │   ├── train.csv
│   │   ├── shard_manifest.csv
│   │   └── shards/train-00000.tar ... train-00099.tar
│   └── eval/
│       ├── eval.csv
│       ├── shard_manifest.csv
│       └── shards/eval-00000.tar
└── RealData/
    ├── eval.csv
    └── videos/*.mp4

All paths in the CSV files are relative to their split directory. Extracting a UEData shard inside its split directory restores the referenced videos/... paths. Every tar shard has a neighboring .sha256 checksum file, and shard_manifest.csv maps every video path to its shard.

Quick Start

Install the required clients:

pip install -U huggingface_hub datasets pandas

Read Metadata Only

The Hugging Face datasets integration loads the CSV metadata without downloading the large video shards:

from datasets import load_dataset

ue = load_dataset("KlingTeam/UniBench", "uedata")
real = load_dataset("KlingTeam/UniBench", "realdata")

print(ue["train"][0])       # UEData reference/train metadata
print(ue["validation"][0])  # UEData evaluation metadata
print(real["test"][0])      # RealData evaluation metadata

Download Evaluation Data

Download only the held-out UEData split and the public real-video subset:

hf download KlingTeam/UniBench \
  --repo-type dataset \
  --include "UniBench/UEData/eval/**" \
  --include "UniBench/RealData/**" \
  --include "UniBench/manifest.json" \
  --local-dir ./unibench

Verify and extract the UEData evaluation shard:

cd ./unibench/UniBench/UEData/eval
(cd shards && sha256sum -c eval-00000.tar.sha256)
tar -xf shards/eval-00000.tar

After extraction, paths such as videos/example.mp4 in eval.csv resolve relative to ./unibench/UniBench/UEData/eval/.

Download the Full Benchmark

The full release is approximately 375 GiB. Make sure the destination has enough free space for both the downloaded tar files and extracted videos.

hf download KlingTeam/UniBench \
  --repo-type dataset \
  --local-dir ./unibench

Extract all UEData shards:

cd ./unibench/UniBench/UEData/train
for shard in shards/*.tar; do tar -xf "$shard"; done

cd ../eval
for shard in shards/*.tar; do tar -xf "$shard"; done

To save space, use shard_manifest.csv to identify and download only the shards needed for selected cases.

Metadata Format

Column	Description
`ceph_path`	Relative path to the RGB video
`caption_list`	JSON-encoded list of text captions
`raft`	Relative path to the optical-flow video; empty for RealData
`depth`	Relative path to the depth video; empty for RealData
`duration`	Duration in seconds
`fps`	Frames per second
`height`	Video height in pixels
`width`	Video width in pixels

Example for resolving one extracted UEData case:

import json
from pathlib import Path

import pandas as pd

split_dir = Path("./unibench/UniBench/UEData/eval")
row = pd.read_csv(split_dir / "eval.csv").iloc[0]

sample = {
    "rgb": split_dir / row["ceph_path"],
    "raft": split_dir / row["raft"],
    "depth": split_dir / row["depth"],
    "caption": json.loads(row["caption_list"])[0],
}
print(sample)

Citation

If you use UniBench or OpenUni, please cite:

@article{huang2025unityvideo,
  title={UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation},
  author={Huang, Jiehui and Zhang, Yuechen and He, Xu and Gao, Yuan and Cen, Zhi and Xia, Bin and Zhou, Yan and Tao, Xin and Wan, Pengfei and Jia, Jiaya},
  journal={arXiv preprint arXiv:2512.07831},
  year={2025}
}

License and Source Data

This repository is released under the Apache 2.0 license. RealData samples are drawn from the public Koala36M dataset. Users are also responsible for complying with the terms of any applicable upstream datasets and for using the benchmark for lawful research purposes.