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:
this http URL

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。