Add image-to-video task category, paper link and sample usage

Hi! I'm Niels from the community science team at Hugging Face.

This PR improves the dataset card by:
- Adding `task_categories: image-to-video` to the metadata for better discoverability.
- Updating the paper link to point to the Hugging Face paper page.
- Adding a "Sample Usage" section with instructions from the official repository on how to use the evaluation toolkit with this benchmark.

README.md

@@ -1,12 +1,15 @@
 ---
-license: apache-2.0
 language:
 - en
-tags:
-- video-generation
-pretty_name: VBVR-Bench-Data
+license: apache-2.0
 size_categories:
 - n<1K
+task_categories:
+- image-to-video
+pretty_name: VBVR-Bench-Data
+tags:
+- video-generation
+- video-reasoning
 configs:
 - config_name: VBVR-Bench-Data
   data_files:
@@ -17,19 +20,19 @@ configs:
 # VBVR: A Very Big Video Reasoning Suite
 
 <a href="https://video-reason.com" target="_blank">
-    <img alt="Code" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
+    <img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
 </a>
-<a href="https://github.com/orgs/Video-Reason/repositories" target="_blank">
+<a href="https://github.com/Video-Reason/VBVR-EvalKit" target="_blank">
     <img alt="Code" src="https://img.shields.io/badge/VBVR-Code-100000?style=flat-square&logo=github&logoColor=white" height="20" />
 </a>
-  <a href="https://arxiv.org/abs/2602.20159" target="_blank">
-      <img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR-red?logo=arxiv" height="20" />
+  <a href="https://huggingface.co/papers/2602.20159" target="_blank">
+      <img alt="Paper" src="https://img.shields.io/badge/Paper-HF-red?logo=huggingface" height="20" />
   </a>
 <a href="https://huggingface.co/Video-Reason/VBVR-Wan2.2" target="_blank">
-    <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Wan2.2-Model-ffc107?color=ffc107&logoColor=white" height="20" />
+    <img alt="Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Wan2.2-Model-ffc107?color=ffc107&logoColor=white" height="20" />
 </a>
 <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Dataset" target="_blank">
-    <img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Dataset-Data-ffc107?color=ffc107&logoColor=white" height="20" />
+    <img alt="Data" 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/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" />
@@ -43,22 +46,35 @@ To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, a
 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.**
 
+For more details, please refer to the paper: [A Very Big Video Reasoning Suite](https://huggingface.co/papers/2602.20159).
+
+## Sample Usage
+
+To evaluate a model using the VBVR suite, you can use the official evaluation toolkit [VBVR-EvalKit](https://github.com/Video-Reason/VBVR-EvalKit):
+
+```bash
+# Install the toolkit
+git clone https://github.com/Video-Reason/VBVR-EvalKit.git && cd VBVR-EvalKit
+python -m venv venv && source venv/bin/activate
+pip install -e .
+
+# Setup a model (example: SVD)
+bash setup/install_model.sh --model svd --validate
+
+# Inference
+python examples/generate_videos.py --questions-dir /path/to/VBVR-Bench-Data --output-dir ./outputs --model svd
+
+# Evaluation (VBVR-Bench)
+python examples/score_videos.py --inference-dir ./outputs
+```
 
 ## Release Information
 We are pleased to release the official **VBVR-Bench** test dataset, designed for standardized and rigorous evaluation of video-based visual reasoning models. 
-The test split is designed along with the evaluation toolkit provided by Video-Reason at [VBVR-Bench evaluation code](https://github.com/Video-Reason/VBVR-Bench).
+The test split is designed along with the evaluation toolkit provided by Video-Reason at [VBVR-EvalKit](https://github.com/Video-Reason/VBVR-EvalKit).
 
 After running evaluation, you can compare your model’s performance on the public leaderboard at [VBVR-Bench Leaderboard](https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard).
 
-In this release, we present 
-[**VBVR-Wan2.2**](https://huggingface.co/Video-Reason/VBVR-Wan2.2), 
-[**VBVR-Dataset**](https://huggingface.co/datasets/Video-Reason/VBVR-Dataset),
-[**VBVR-Bench-Data**](https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data) and 
-[**VBVR-Bench-Leaderboard**](https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard).
-
 ## Data Structure
 The dataset is organized by domain and task generator. For example:
 
@@ -71,33 +87,40 @@ In-Domain_50/
       ground_truth.mp4
       prompt.txt
 ```
-Structure Description
-
-- In-Domain_50/Out-of-Domain_50: 
-Evaluation splits indicating whether samples belong to in-domain or out-of-domain settings.
-
-- G-XXX_task-name_data-generator: 
-A specific reasoning task category and its corresponding data generator.
-
-- 00000-00004: 
-Individual sample instances.
-
-Each sample directory contains
-- first_frame.png: The initial frame of the video
-
-- final_frame.png: The final frame
+### Structure Description
 
-- ground_truth.mp4: The full video sequence
+- **In-Domain_50/Out-of-Domain_50**: Evaluation splits indicating whether samples belong to in-domain or out-of-domain settings.
+- **G-XXX_task-name_data-generator**: A specific reasoning task category and its corresponding data generator.
+- **00000-00004**: Individual sample instances.
 
-- prompt.txt: The textual reasoning question or instruction
+Each sample directory contains:
+- `first_frame.png`: The initial frame of the video
+- `final_frame.png`: The final frame
+- `ground_truth.mp4`: The full video sequence
+- `prompt.txt`: The textual reasoning question or instruction
 
 ## 🖊️ Citation
 
-```bib
+```bibtex
 @article{vbvr2026,
-      title={A Very Big Video Reasoning Suite}, 
-      author={Maijunxian Wang and Ruisi Wang and Juyi Lin and Ran Ji and Thaddäus Wiedemer and Qingying Gao and Dezhi Luo and Yaoyao Qian and Lianyu Huang and Zelong Hong and Jiahui Ge and Qianli Ma and Hang He and Yifan Zhou and Lingzi Guo and Lantao Mei and Jiachen Li and Hanwen Xing and Tianqi Zhao and Fengyuan Yu and Weihang Xiao and Yizheng Jiao and Jianheng Hou and Danyang Zhang and Pengcheng Xu and Boyang Zhong and Zehong Zhao and Gaoyun Fang and John Kitaoka and Yile Xu and Hua Xu and Kenton Blacutt and Tin Nguyen and Siyuan Song and Haoran Sun and Shaoyue Wen and Linyang He and Runming Wang and Yanzhi Wang and Mengyue Yang and Ziqiao Ma and Raphaël Millière and Freda Shi and Nuno Vasconcelos and Daniel Khashabi and Alan Yuille and Yilun Du and Ziming Liu and Bo Li and Dahua Lin and Ziwei Liu and Vikash Kumar and Yijiang Li and Lei Yang and Zhongang Cai and Hokin Deng},
+  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 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
+             Bo Li and Dahua Lin and Ziwei Liu and Vikash Kumar and Yijiang Li and
+             Lei Yang and Zhongang Cai and Hokin Deng},
   journal = {arXiv preprint arXiv:2602.20159},
-  year = {2026}
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2602.20159}
 }
 ```