Add comprehensive model card for Hulk

This PR significantly enhances the model card for the `OpenGVLab/Hulk` model.

It includes:
- Adding `pipeline_tag: any-to-any` to classify the model's generalist, multimodal capabilities.
- Adding `library_name: transformers` to indicate compatibility with the Transformers library and enable the "Use in Transformers" widget.
- Adding comprehensive tags like `human-centric`, `multimodal`, `2d-vision`, `3d-vision`, `vision-language`, and various task-specific tags for better discoverability.
- Incorporating the full paper abstract, project page, and GitHub repository links for detailed information.
- Including key visuals (teaser and framework images) from the GitHub repository.
- Providing clear guidance on where to find usage examples, training, and evaluation details (the official GitHub repository).
- Adding the academic citation.

These changes will greatly improve the model's visibility and utility on the Hugging Face Hub.

README.md

@@ -1,3 +1,57 @@
 ---
 license: mit
+pipeline_tag: any-to-any
+library_name: transformers
+tags:
+  - human-centric
+  - multimodal
+  - 2d-vision
+  - 3d-vision
+  - skeleton-based
+  - vision-language
+  - pose-estimation
+  - object-detection
+  - image-segmentation
+  - action-recognition
+  - image-captioning
+  - attribute-recognition
 ---
+
+# Hulk: A Universal Knowledge Translator for Human-Centric Tasks
+
+This model was presented in the paper [Hulk: A Universal Knowledge Translator for Human-Centric Tasks](https://huggingface.co/papers/2312.01697).
+
+*   **Project Page**: [https://humancentricmodels.github.io/Hulk/](https://humancentricmodels.github.io/Hulk/)
+*   **GitHub Repository**: [https://github.com/OpenGVLab/Hulk](https://github.com/OpenGVLab/Hulk)
+*   **ArXiv Paper**: [https://arxiv.org/abs/2312.01697](https://arxiv.org/abs/2312.01697)
+
+<p align="center">
+    <img src="https://huggingface.co/OpenGVLab/Hulk/resolve/main/assets/teaser.png" width="1000" />
+</p>
+
+## Abstract
+Human-centric perception tasks, e.g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis. There is a recent surge to develop human-centric foundation models that can benefit a broad range of human-centric perception tasks. While many human-centric foundation models have achieved success, they did not explore 3D and vision-language tasks for human-centric and required task-specific finetuning. These limitations restrict their application to more downstream tasks and situations. To tackle these problems, we present Hulk, the first multimodal human-centric generalist model, capable of addressing 2D vision, 3D vision, skeleton-based, and vision-language tasks without task-specific finetuning. The key to achieving this is condensing various task-specific heads into two general heads, one for discrete representations, \emph{e.g.,} languages, and the other for continuous representations, \emph{e.g.,} location coordinates. The outputs of two heads can be further stacked into four distinct input and output modalities. This uniform representation enables Hulk to treat diverse human-centric tasks as modality translation, integrating knowledge across a wide range of tasks. Comprehensive evaluations of Hulk on 12 benchmarks covering 8 human-centric tasks demonstrate the superiority of our proposed method, achieving state-of-the-art performance in 11 benchmarks.
+
+## Model Framework
+<p align="center">
+    <img src="https://huggingface.co/OpenGVLab/Hulk/resolve/main/assets/framework.png" width="1000" />
+</p>
+
+## Usage
+For detailed installation instructions, dataset preparation, training procedures, evaluation scripts, and comprehensive inference examples across various human-centric tasks, please refer to the official [Hulk GitHub repository](https://github.com/OpenGVLab/Hulk).
+
+The codebase is built on top of the 🤗 [Diffusers](https://github.com/huggingface/diffusers) and 🤗 [Transformers](https://github.com/huggingface/transformers) libraries, and users should consult the repository for specific usage patterns.
+
+## Model Performance
+Hulk has achieved state-of-the-art results on various human-centric benchmarks, demonstrating its superiority in both direct evaluation and fine-tuning scenarios. For detailed performance metrics across different tasks and datasets, please consult the tables in the [GitHub README](https://github.com/OpenGVLab/Hulk#model-performance) and the [original paper](https://huggingface.co/papers/2312.01697).
+
+## Citation
+If you find this work useful, please consider citing:
+```bibtex
+@article{wang2023hulk,
+  title={Hulk: A Universal Knowledge Translator for Human-Centric Tasks},
+  author={Wang, Yizhou and Wu, Yixuan and Tang, Shixiang and He, Weizhen and Guo, Xun and Zhu, Feng and Bai, Lei and Zhao, Rui and Wu, Jian and He, Tong and others},
+  journal={arXiv preprint arXiv:2312.01697},
+  year={2023}
+}
+```