DivAvatar: Diverse 3D Avatar Generation with a Single Prompt

Weijing Tao, Biwen Lei, Kunhao Liu, Shijian Lu, Miaomiao Cui, Xuansong Xie

Abstract: Text-to-Avatar generation has recently made significant strides due to advancements in diffusion models. However, most existing works remain constrained by limited diversity, producing avatars with subtle differences in appearance for a given text prompt. We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a multitude of distinct and richly varied 3D avatars from a single text prompt. Different from most existing works that exploit scene-specific 3D representations such as NeRF, DivAvatar finetunes a 3D generative model (i.e., EVA3D), allowing diverse avatar generation from simply noise sampling in inference time. DivAvatar has two key designs that help achieve generation diversity and visual quality. The first is a noise sampling technique during

External IDs:dblp:conf/wacv/TaoLLLCX25