HQ-Avatar: Towards High-Quality 3D Avatar Generation via Point-based Representation

Weitian Zhang, Sijing Wu, Yichao Yan, Ben Xue, Wenhan Zhu, Xiaokang Yang

Abstract: Despite the flourishing of 3D object generation, generating high-quality digital avatars with detailed geometry and texture that are free to animate remains a challenging task. Existing avatar generation techniques often suffer from limitations such as low-quality geometry and blurry texture. Thus, we propose HQ-Avatar, a novel method for generating animatable avatars with high-quality geometry and texture. We enhance the geometry quality by proposing an importance sampling strategy and capturing intricate details through learned normal maps. To achieve high-quality texture, we present a neural point-based avatar representation, which enables high-resolution rendering results at a resolution of 1024 2 , allowing detailed supervision. Extensive experiments on THuman2.0 dataset demonstrate the superiority of our method over state-of-the-art techniques in generating high-quality avatars. Furthermore, we show the applicability of our method by employing it as 3D priors to simplify the human avatar reconstruction process from scans or even single images. Code is available at https://github.com/olivia23333/HQ-Avatar