AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose
Huichao Zhang, Bowen Chen, Hao Yang, Liao Qu, Xu Wang, Li Chen, Chao, Long, Feida Zhu, Kang Du, Min Zheng

TL;DR
AvatarVerse introduces a novel pipeline that generates high-quality, expressive 3D avatars from text and pose guidance, utilizing a 2D diffusion model conditioned on DensePose for view consistency and a progressive synthesis strategy.
Contribution
The paper presents a zero-shot 3D avatar creation method combining DensePose-conditioned diffusion and progressive high-resolution synthesis, improving quality and stability over prior approaches.
Findings
Outperforms previous methods in avatar fidelity and expressiveness.
Achieves stable, view-consistent 3D avatar generation from text and pose.
User studies confirm superior quality and realism.
Abstract
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…
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Code & Models
Videos
Taxonomy
TopicsHuman Motion and Animation · Human Pose and Action Recognition · Advanced Vision and Imaging
MethodsDiffusion
