XAGen: 3D Expressive Human Avatars Generation
Zhongcong Xu Jianfeng Zhang Jun Hao Liew Jiashi Feng Mike Zheng Shou
Recent advances in 3D-aware GAN models have enabled the generation of
realistic and controllable human body images. However, existing methods focus
on the control of major body joints, neglecting the manipulation of expressive
attributes, such as facial expressions, jaw poses, hand poses, and so on. In
this work, we present XAGen, the first 3D generative model for human avatars
capable of expressive control over body, face, and hands. To enhance the
fidelity of small-scale regions like face and hands, we devise a multi-scale
and multi-part 3D representation that models fine details. Based on this
representation, we propose a multi-part rendering technique that disentangles
the synthesis of body, face, and hands to ease model training and enhance
geometric quality. Furthermore, we design multi-part discriminators that
evaluate the quality of the generated avatars with respect to their appearance
and fine-grained control capabilities. Experiments show that XAGen surpasses
state-of-the-art methods in terms of realism, diversity, and expressive control
abilities. Code and data will be made available at
https://showlab.github.io/xagen.