We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we ``carve'' the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety.

提出了一种基于扩散模型的 3D 生成方法，通过生成前后面衣着人的真实性的法线贴图，使用优化网格的方式实现人物的 3D 重建，还结合最新的文本到图像扩散模型，实现了对人物身份的控制，能够生成具有更好感知效果和身份多样性的真实 3D 服装人物。

使用二维扩散概率模型从皮肤形状先验中雕刻三维服装人体