We present Disco4D, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancing the generation details and flexibility. It has the following technical innovations. 1) Disco4D learns to efficiently fit the clothing Gaussians over the SMPL-X Gaussians. 2) It adopts diffusion models to enhance the 3D generation process, \textit{e.g.}, modeling occluded parts not visible in the input image. 3) It learns an identity encoding for each clothing Gaussian to facilitate the separation and extraction of clothing assets. Furthermore, Disco4D naturally supports 4D human animation with vivid dynamics. Extensive experiments demonstrate the superiority of Disco4D on 4D human generation and animation tasks. Our visualizations can be found in \url{this https URL}.
我们提出了Disco4D，一个用于从单张图像生成和动画化4D人类的全新高斯分布框架。与现有方法不同，Disco4D 通过将服装（使用高斯模型）与人体（使用SMPL-X模型）明确解耦，大大提升了生成细节和灵活性。它具有以下技术创新：1) Disco4D 学会高效地将服装高斯拟合到 SMPL-X 高斯上。 2) 它采用扩散模型增强了三维生成过程，例如对输入图像中不可见的遮挡部分进行建模。 3) 它为每个服装高斯学习一个身份编码，以便于服装资产的分离和提取。此外，Disco4D 自然支持带有生动动态的 4D 人体动画。大量实验表明，Disco4D 在 4D 人类生成和动画任务上表现出卓越的优势。