File size: 11,653 Bytes
0f43af4 f3406a5 0f43af4 f3406a5 0f43af4 f3406a5 0f43af4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | ---
base_model:
- Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
library_name: diffusers
license: apache-2.0
pipeline_tag: image-to-video
datasets:
- Video-Reason/VBVR-Dataset
---
# VBVR: A Very Big Video Reasoning Suite
<a href="https://video-reason.com" target="_blank">
<img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
</a>
<a href="https://github.com/Video-Reason/VBVR-EvalKit" target="_blank">
<img alt="Code" src="https://img.shields.io/badge/Evaluation_code-VBVR_Bench-100000?style=flat-square&logo=github&logoColor=white" height="20" />
</a>
<a href="https://github.com/Video-Reason/VBVR-Wan2.2" target="_blank">
<img alt="Code" src="https://img.shields.io/badge/Training_code-VBVR_Wan2.2-100000?style=flat-square&logo=github&logoColor=white" height="20" />
</a>
<a href="https://github.com/Video-Reason/VBVR-DataFactory" target="_blank">
<img alt="Code" src="https://img.shields.io/badge/Data_code-VBVR_DataFactory-100000?style=flat-square&logo=github&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/papers/2602.20159" target="_blank">
<img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR-red?logo=arxiv" height="20" />
</a>
<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Dataset" target="_blank">
<img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Dataset-Data-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data" target="_blank">
<img alt="Bench Data" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Bench-Data-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
<a href="https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard" target="_blank">
<img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Bench-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
</a>
## Overview
Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture,
enabling intuitive reasoning over motion, interaction, and causality. Rapid progress in video models has focused primarily on visual quality.
Systematically studying video reasoning and its scaling behavior suffers from a lack of video reasoning (training) data.
To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks
and over one million video clips—approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench,
a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers,
enabling reproducible and interpretable diagnosis of video reasoning capabilities.
Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization
to unseen reasoning tasks. **Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning.**
The model was presented in the paper [A Very Big Video Reasoning Suite](https://huggingface.co/papers/2602.20159).
## Models Zoo
| Model | Base Architecture | Other Remarks |
|-------|-------------------|---------------|
| [VBVR-Wan2.1](https://huggingface.co/Video-Reason/VBVR-Wan2.1) | Wan2.1-I2V-14B-720P | Diffusers format |
| [VBVR-Wan2.2](https://huggingface.co/Video-Reason/VBVR-Wan2.2) | Wan2.2-I2V-A14B | Diffusers format |
| [**VBVR-Wan2.1-diffsynth**](https://huggingface.co/Video-Reason/VBVR-Wan2.1-diffsynth) | Wan2.1-I2V-14B-720P | DiffSynth LoRA format |
| [VBVR-Wan2.2-diffsynth](https://huggingface.co/Video-Reason/VBVR-Wan2.2-diffsynth) | Wan2.2-I2V-A14B | DiffSynth LoRA format |
| [VBVR-LTX2.3-diffsynth](https://huggingface.co/Video-Reason/VBVR-LTX2.3-diffsynth) | LTX-Video-2.3 | DiffSynth LoRA format |
## Release Information
VBVR-Wan2.1 is trained from Wan2.1-I2V-14B-720P without architectural modifications, as the goal of VBVR is to *investigate data scaling behavior* and provide *strong baseline models* for the video reasoning research community. Leveraging the VBVR-Dataset, which constitutes one of the largest video reasoning datasets to date, the VBVR model family achieved highest scores on VBVR-Bench.
In this release, we present
[**VBVR-Wan2.1**](https://huggingface.co/Video-Reason/VBVR-Wan2.1) (Diffusers format),
[**VBVR-Wan2.1-diffsynth**](https://huggingface.co/Video-Reason/VBVR-Wan2.1-diffsynth) (DiffSynth LoRA format), and
[**VBVR-LTX2.3-diffsynth**](https://huggingface.co/Video-Reason/VBVR-LTX2.3-diffsynth) (DiffSynth LoRA format; Diffusers does not yet support LTX-Video-2.3, so only the DiffSynth LoRA format is released for this model).
<table>
<tr>
<th>Model</th>
<th>Overall</th>
<th>ID</th>
<th>ID-Abst.</th>
<th>ID-Know.</th>
<th>ID-Perc.</th>
<th>ID-Spat.</th>
<th>ID-Trans.</th>
<th>OOD</th>
<th>OOD-Abst.</th>
<th>OOD-Know.</th>
<th>OOD-Perc.</th>
<th>OOD-Spat.</th>
<th>OOD-Trans.</th>
</tr>
<tbody>
<tr>
<td><strong>Human</strong></td>
<td>0.974</td><td>0.960</td><td>0.919</td><td>0.956</td><td>1.00</td><td>0.95</td><td>1.00</td>
<td>0.988</td><td>1.00</td><td>1.00</td><td>0.990</td><td>1.00</td><td>0.970</td>
</tr>
<tr style="background:#F2F0EF;font-weight:700;text-align:center;">
<td colspan="14"><em>Open-source Models</em></td>
</tr>
<tr>
<td>CogVideoX1.5-5B-I2V</td>
<td>0.273</td><td>0.283</td><td>0.241</td><td>0.328</td><td>0.257</td><td>0.328</td><td>0.305</td>
<td>0.262</td><td><u>0.281</u></td><td>0.235</td><td>0.250</td><td><strong>0.254</strong></td><td>0.282</td>
</tr>
<tr>
<td>HunyuanVideo-I2V</td>
<td>0.273</td><td>0.280</td><td>0.207</td><td>0.357</td><td>0.293</td><td>0.280</td><td><u>0.316</u></td>
<td>0.265</td><td>0.175</td><td><strong>0.369</strong></td><td>0.290</td><td><u>0.253</u></td><td>0.250</td>
</tr>
<tr>
<td><strong>Wan2.2-I2V-A14B</strong></td>
<td><strong>0.371</strong></td><td><strong>0.412</strong></td><td><strong>0.430</strong></td>
<td><strong>0.382</strong></td><td><strong>0.415</strong></td><td><strong>0.404</strong></td>
<td><strong>0.419</strong></td><td><strong>0.329</strong></td>
<td><strong>0.405</strong></td><td>0.308</td><td><strong>0.343</strong></td>
<td>0.236</td><td><u>0.307</u></td>
</tr>
<tr>
<td><u>LTX-2</u></td>
<td><u>0.313</u></td><td><u>0.329</u></td><td><u>0.316</u></td>
<td><u>0.362</u></td><td><u>0.326</u></td><td><u>0.340</u></td>
<td>0.306</td><td><u>0.297</u></td>
<td>0.244</td><td><u>0.337</u></td><td><u>0.317</u></td>
<td>0.231</td><td><strong>0.311</strong></td>
</tr>
<tr style="background:#F2F0EF;font-weight:700;text-align:center;">
<td colspan="14"><em>Proprietary Models</em></td>
</tr>
<tr>
<td><u>Seedance 2.0</u></td>
<td><u>0.544</u></td><td><strong>0.570</strong></td><td>0.593</td><td><u>0.498</u></td><td><strong>0.618</strong></td><td><u>0.514</u></td><td><strong>0.602</strong></td>
<td><u>0.517</u></td><td><strong>0.643</strong></td><td>0.398</td><td><u>0.492</u></td><td>0.427</td><td><strong>0.556</strong></td>
</tr>
<tr>
<td>Runway Gen-4 Turbo</td>
<td>0.403</td><td>0.392</td><td>0.396</td><td>0.409</td><td>0.429</td><td>0.341</td><td>0.363</td>
<td>0.414</td><td>0.515</td><td><u>0.429</u></td><td>0.419</td><td>0.327</td><td>0.373</td>
</tr>
<tr>
<td><strong>Sora 2</strong></td>
<td><strong>0.546</strong></td><td><u>0.569</u></td><td><u>0.602</u></td>
<td>0.477</td><td><u>0.581</u></td><td><strong>0.572</strong></td>
<td><u>0.597</u></td><td><strong>0.523</strong></td>
<td><u>0.546</u></td><td><strong>0.472</strong></td><td><strong>0.525</strong></td>
<td><strong>0.462</strong></td><td><u>0.546</u></td>
</tr>
<tr>
<td>Kling 2.6</td>
<td>0.369</td><td>0.408</td><td>0.465</td><td>0.323</td><td>0.375</td><td>0.347</td><td>0.519</td>
<td>0.330</td><td>0.528</td><td>0.135</td><td>0.272</td><td>0.356</td><td>0.359</td>
</tr>
<tr>
<td>Veo 3.1</td>
<td>0.480</td><td>0.531</td><td><strong>0.611</strong></td>
<td><strong>0.503</strong></td><td>0.520</td><td>0.444</td>
<td>0.510</td><td>0.429</td>
<td><u>0.577</u></td><td>0.277</td><td>0.420</td>
<td><u>0.441</u></td><td>0.404</td>
</tr>
<tr style="background:#F2F0EF;font-weight:700;text-align:center;">
<td colspan="14"><em>Data Scaling Strong Baseline</em></td>
</tr>
<tr>
<td><strong>VBVR-LTX2.3</strong></td>
<td>0.516</td><td>0.580</td><td>0.608</td><td>0.631</td><td>0.529</td><td>0.454</td><td>0.680</td>
<td>0.453</td><td>0.608</td><td>0.577</td><td><u>0.409</u></td><td>0.414</td><td><u>0.388</u></td>
</tr>
<tr>
<td><strong>VBVR-Wan2.1</strong></td>
<td><u>0.592</u></td><td><u>0.724</u></td><td><u>0.705</u></td><td><u>0.710</u></td><td><u>0.727</u></td><td><u>0.719</u></td><td><u>0.784</u></td>
<td><u>0.461</u></td><td><u>0.674</u></td><td><strong>0.592</strong></td><td>0.387</td><td><u>0.461</u></td><td>0.387</td>
</tr>
<tr>
<td><strong>VBVR-Wan2.2</strong></td>
<td><strong>0.685</strong></td><td><strong>0.760</strong></td><td><strong>0.724</strong></td>
<td><strong>0.750</strong></td><td><strong>0.782</strong></td><td><strong>0.745</strong></td>
<td><strong>0.833</strong></td><td><strong>0.610</strong></td>
<td><strong>0.768</strong></td><td><u>0.572</u></td><td><strong>0.547</strong></td>
<td><strong>0.618</strong></td><td><strong>0.615</strong></td>
</tr>
</tbody>
</table>
## QuickStart
### Inference
For running inference, please refer to the [**official guide**](https://github.com/Video-Reason/VBVR-Wan2.2?tab=readme-ov-file#wan21-inference) in the VBVR-Wan2.2 GitHub repository.
This repository contains the latest instructions, configurations, and examples for performing inference with the VBVR family models.
## Citation
```bibtex
@article{vbvr2026,
title = {A Very Big Video Reasoning Suite},
author = {Wang, Maijunxian and Wang, Ruisi and Lin, Juyi and Ji, Ran and
Wiedemer, Thadd{\"a}us and Gao, Qingying and Luo, Dezhi and
Qian, Yaoyao and Huang, Lianyu and Hong, Zelong and Ge, Jiahui and
Ma, Qianli and He, Hang and Zhou, Yifan and Guo, Lingzi and
Mei, Lantao and Li, Jiachen and Xing, Hanwen and Zhao, Tianqi and
Yu, Fengyuan and Xiao, Weihang and Jiao, Yizheng and
Hou, Jianheng and Zhang, Danyang and Xu, Pengcheng and
Zhong, Boyang and Zhao, Zehong and Fang, Gaoyun and Kitaoka, John and
Xu, Yile and Xu, Hua bureau and Blacutt, Kenton and Nguyen, Tin and
Song, Siyuan and Sun, Haoran and Wen, Shaoyue and He, Linyang and
Wang, Runming and Wang, Yanzhi and Yang, Mengyue and Ma, Ziqiao and
Milli{\`e}re, Rapha{\"e}l and Shi, Freda and Vasconcelos, Nuno and
Khashabi, Daniel and Yuille, Alan and Du, Yilun and Liu, Ziming and
Lin, Dahua and Liu, Ziwei and Kumar, Vikash and Li, Yijiang and
Yang, Lei and Cai, Zhongang and Deng, Hokin},
journal = {arXiv preprint arXiv:2602.20159},
year = {2026},
url = {https://arxiv.org/abs/2602.20159}
}
``` |