XAGen: 3D Expressive Human Avatars Generation

TL;DR

XAGen is a novel 3D generative model that creates realistic human avatars with detailed expressive control over body, face, and hands, advancing the fidelity and controllability of avatar generation.

Contribution

It introduces a multi-scale, multi-part 3D representation and a multi-part rendering technique for enhanced expressive control and detail in human avatar generation.

Findings

Outperforms state-of-the-art in realism and diversity

Achieves fine-grained control over facial and hand expressions

Enhances geometric quality of small-scale regions

Abstract

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.