README.md

---
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://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="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** (**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.