  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: https://avatarverse3d.github.io
