Add image-to-video task category and improve dataset card structure

Hi! I'm Niels, part of the community science team at Hugging Face.

I've opened this PR to improve the dataset card for OmniVCus-Test. Specifically, I've:
- Added `task_categories: image-to-video` to the metadata to improve discoverability.
- Consolidated the links to the project page, paper, and code at the top for easier navigation.
- Organized the related Hugging Face artifacts (training data and models) for better visibility.

This ensures the dataset is correctly categorized and easier for researchers to use.

README.md

@@ -1,7 +1,7 @@
 ---
-license: apache-2.0
 language:
 - en
+license: apache-2.0
 size_categories:
 - n<1K
 modalities:
@@ -9,17 +9,23 @@ modalities:
 - image
 - text
 arxiv: 2506.23361
+task_categories:
+- image-to-video
+tags:
+- video-customization
+- subject-driven
+- multi-modal
 ---
 
-# [NeurIPS 2025] OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
+# OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
 
-## Dataset Description
+[**Project Page**](https://caiyuanhao1998.github.io/project/OmniVCus/) | [**Paper**](https://huggingface.co/papers/2506.23361) | [**Code**](https://github.com/caiyuanhao1998/Open-OmniVCus) | [**Models**](https://huggingface.co/CaiYuanhao/OmniVCus)
 
-This is a testing dataset for multi-modal control video generation. It contains 648 manually collected and annotated data samples to support 
-reference-to-video, reference-mask-to-video, reference-depth-to-video, and reference-instruction-to-video customization.
+## Dataset Description
 
-Here is the data overview:
+OmniVCus-Test is a testing dataset for multi-modal control video generation. It contains 648 manually collected and annotated data samples to support reference-to-video, reference-mask-to-video, reference-depth-to-video, and reference-instruction-to-video customization.
 
+### Data Overview
 
 | Task | #Subject | Number | Path |
 |:----:|:--------:|:------:|:----:|
@@ -32,48 +38,17 @@ Here is the data overview:
 | Reference-depth-to-video | 3 | 40 | [`depth/three_subject`](https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test/tree/main/depth/multiple_subject) |
 | Reference-instruction-to-video | 1 | 113 | [`instruct_edit/single_subject`](https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test/tree/main/instruct_edit/single_subject) |
 
+## Related Resources
 
-## Training Data Link
-
-We also release a training dataset on HuggingFace at
-
-https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train
-
-
-## Github Code Link
-
-This dataset is intended to be used together with our code. Please refer to the GitHub repository below for more detailed instructions.
-
-https://github.com/caiyuanhao1998/Open-OmniVCus
-
-
-## Huggingface Model Link
-
-We also release three models based on Wan2.1-1.3B, Wan2.1-14B, and Wan2.2-14B in the following link:
-
-https://huggingface.co/CaiYuanhao/OmniVCus
-
-
-## Project Page Link
-
-For more video customization results, please refer to our project page:
-
-https://caiyuanhao1998.github.io/project/OmniVCus/
-
-
-## Arxiv Paper Link
-
-For more technical details, please refer to our NeurIPS 2025 paper:
-
-https://arxiv.org/abs/2506.23361
-
-
+- **Training Data:** [OmniVCus-Train](https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train)
+- **Hugging Face Models:** [OmniVCus Models](https://huggingface.co/CaiYuanhao/OmniVCus) (Wan2.1/2.2-based)
+- **GitHub Repository:** [Open-OmniVCus](https://github.com/caiyuanhao1998/Open-OmniVCus)
 
 ## Citation
 
 If you find our code, data, and models useful, please consider citing our paper:
 
-```sh
+```bibtex
 @inproceedings{omnivcus,
   title={OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions},
   author={Yuanhao Cai and He Zhang and Xi Chen and Jinbo Xing and Kai Zhang and Yiwei Hu and Yuqian Zhou and Zhifei Zhang and Soo Ye Kim and Tianyu Wang and Yulun Zhang and Xiaokang Yang and Zhe Lin and Alan Yuille},