Add pipeline tag, library name and link to paper

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

This PR improves the model card for UNICA by:
- Adding the `image-to-3d` pipeline tag to ensure it is correctly categorized.
- Adding `library_name: diffusers` as the configuration files indicate compatibility with the Diffusers library.
- Linking the model card to the associated research paper.

Please let me know if you have any questions!

README.md

@@ -1,18 +1,30 @@
 ---
 license: mit
+pipeline_tag: image-to-3d
+library_name: diffusers
 ---
 
 <h1 align="center">UNICA: A Unified Neural Framework for Controllable 3D Avatars</h1>
 
 <p align="center">
   <a href="https://github.com/zjh21/UNICA"><img src="https://img.shields.io/badge/GitHub-Code-blue?logo=github&logoColor=white" alt="GitHub"></a>
-  <!-- <a href="https://arxiv.org/abs/XXXX.XXXXX"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white" alt="arXiv"></a> -->
+  <a href="https://huggingface.co/papers/2604.02799"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white" alt="arXiv"></a>
 </p>
 
 <p align="center">
-  <img src="assets/teaser.png" alt="Teaser" width="100%">
+  <img src="https://huggingface.co/zjh21/UNICA/resolve/main/assets/teaser.png" alt="Teaser" width="100%">
 </p>
 
 ## Abstract
 
-Controllable 3D human avatars have found widespread applications in 3D games, the metaverse, and AR/VR scenarios. The conventional approach to creating such a 3D avatar requires a lengthy, intricate pipeline encompassing appearance modeling, motion planning, rigging, and physical simulation. In this paper, we introduce UNICA (UNIfied neural Controllable Avatar), a skeleton-free generative model that unifies all avatar control components into a single neural framework. Given keyboard inputs akin to video game controls, UNICA generates the next frame of a 3D avatar's geometry through an action-conditioned diffusion model operating on 2D position maps. A point transformer then maps the resulting geometry to 3D Gaussian Splatting for high-fidelity free-view rendering. Our approach naturally captures hair and loose clothing dynamics without manually designed physical simulation, and supports extra-long autoregressive generation. To the best of our knowledge, UNICA is the first model to unify the workflow of "motion planning, rigging, physical simulation, and rendering".
+Controllable 3D human avatars have found widespread applications in 3D games, the metaverse, and AR/VR scenarios. The conventional approach to creating such a 3D avatar requires a lengthy, intricate pipeline encompassing appearance modeling, motion planning, rigging, and physical simulation. In this paper, we introduce **UNICA** (**UNI**fied neural **C**ontrollable **A**vatar), a skeleton-free generative model that unifies all avatar control components into a single neural framework. Given keyboard inputs akin to video game controls, UNICA generates the next frame of a 3D avatar's geometry through an action-conditioned diffusion model operating on 2D position maps. A point transformer then maps the resulting geometry to 3D Gaussian Splatting for high-fidelity free-view rendering. Our approach naturally captures hair and loose clothing dynamics without manually designed physical simulation, and supports extra-long autoregressive generation. 
+
+## Resources
+- **Paper:** [UNICA: A Unified Neural Framework for Controllable 3D Avatars](https://huggingface.co/papers/2604.02799)
+- **GitHub Repository:** [https://github.com/zjh21/UNICA](https://github.com/zjh21/UNICA)
+
+## Installation and Usage
+
+Please refer to the official [GitHub repository](https://github.com/zjh21/UNICA) for detailed installation instructions and inference scripts. The pipeline generally involves two stages:
+1. **Geometry Generation:** Using the action-conditioned diffusion model to generate position maps.
+2. **Appearance Mapping:** Mapping geometry to 3D Gaussian Splatting via a point transformer for rendering.