Creating expressive, diverse and high-quality 3D avatars from highly
customized text descriptions and pose guidance is a challenging task, due to
the intricacy of modeling and texturing in 3D that ensure details and various
styles (realistic, fictional, etc). We present AvatarVerse, a stable pipeline
for generating expressive high-quality 3D avatars from nothing but text
descriptions and pose guidance. In specific, we introduce a 2D diffusion model
conditioned on DensePose signal to establish 3D pose control of avatars through
2D images, which enhances view consistency from partially observed scenarios.
It addresses the infamous Janus Problem and significantly stablizes the
generation process. Moreover, we propose a progressive high-resolution 3D
synthesis strategy, which obtains substantial improvement over the quality of
the created 3D avatars. To this end, the proposed AvatarVerse pipeline achieves
zero-shot 3D modeling of 3D avatars that are not only more expressive, but also
in higher quality and fidelity than previous works. Rigorous qualitative
evaluations and user studies showcase AvatarVerse's superiority in synthesizing
high-fidelity 3D avatars, leading to a new standard in high-quality and stable
3D avatar creation. Our project page is: this https URL

从高度定制的文本描述和姿势指导中创建富有表达力、多样化和高质量的 3D 头像是一项具有挑战性的任务。我们提出了 AvatarVerse，一个稳定的流水线，可以仅仅通过文本描述和姿势指导生成富有表达力和高质量的 3D 头像。