We study the problem of creating high-fidelity and animatable 3D avatars from only textual descriptions. Existing text-to-avatar methods are either limited to static avatars which cannot be animated or struggle to generate animatable avatars with promising quality and precise pose control. To address these limitations, we propose AvatarStudio, a coarse-to-fine generative model that generates explicit textured 3D meshes for animatable human avatars. Specifically, AvatarStudio begins with a low-resolution NeRF-based representation for coarse generation, followed by incorporating SMPL-guided articulation into the explicit mesh representation to support avatar animation and high resolution rendering. To ensure view consistency and pose controllability of the resulting avatars, we introduce a 2D diffusion model conditioned on DensePose for Score Distillation Sampling supervision. By effectively leveraging the synergy between the articulated mesh representation and the DensePose-conditional diffusion model, AvatarStudio can create high-quality avatars from text that are ready for animation, significantly outperforming previous methods. Moreover, it is competent for many applications, e.g., multimodal avatar animations and style-guided avatar creation. For more results, please refer to our project page: http://jeff95.me/projects/avatarstudio.html

通过AvatarStudio生成高质量的、可动画的3D人物头像，首先使用低分辨率的基于NeRF的表示进行初步生成，然后结合SMPL引导关节活动增加明确的网格表示并支持头像动画和高分辨率渲染，在结果头像中引入基于DensePose的2D扩散模型以确保视角一致性和姿态可控性，利用AvatarStudio可以从文本中生成高质量的头像并具备动态效果，优于之前的方法，并且适用于多模态头像动画和风格引导头像创作等多个应用。

AvatarStudio：高保真和可动画三维头像从文本创建