Fast generation of high-quality 3D digital humans is important to a vast number of applications ranging from entertainment to professional concerns. Recent advances in differentiable rendering have enabled the training of 3D generative models without requiring 3D ground truths. However, the quality of the generated 3D humans still has much room to improve in terms of both fidelity and diversity. In this paper, we present Get3DHuman, a novel 3D human framework that can significantly boost the realism and diversity of the generated outcomes by only using a limited budget of 3D ground-truth data. Our key observation is that the 3D generator can profit from human-related priors learned through 2D human generators and 3D reconstructors. Specifically, we bridge the latent space of Get3DHuman with that of StyleGAN-Human via a specially-designed prior network, where the input latent code is mapped to the shape and texture feature volumes spanned by the pixel-aligned 3D reconstructor. The outcomes of the prior network are then leveraged as the supervisory signals for the main generator network. To ensure effective training, we further propose three tailored losses applied to the generated feature volumes and the intermediate feature maps. Extensive experiments demonstrate that Get3DHuman greatly outperforms the other state-of-the-art approaches and can support a wide range of applications including shape interpolation, shape re-texturing, and single-view reconstruction through latent inversion.

翻译：快速生成高质量三维数字化人体对于从娱乐到专业领域的众多应用至关重要。近年来，可微分渲染技术的进步使得无需三维真实数据即可训练三维生成模型成为可能。然而，生成的三维人体在保真度和多样性方面仍有较大提升空间。本文提出Get3DHuman——一种新颖的三维人体框架，仅需有限的三维真实数据即可显著提升生成结果的真实感与多样性。我们的关键发现是：三维生成器可通过二维人体生成器和三维重建器学习到的人体相关先验而受益。具体而言，我们通过专门设计的先验网络将Get3DHuman的潜空间与StyleGAN-Human的潜空间相连接，其中输入潜码被映射至由像素对齐三维重建器所张成的形状与纹理特征体。随后，先验网络的输出被用作主生成器网络的监督信号。为确保有效训练，我们进一步提出了三种针对生成特征体及中间特征图的定制化损失函数。大量实验表明，Get3DHuman显著优于现有最先进方法，并能支持形状插值、形状纹理重映射及基于潜空间反演的单视图重建等多种应用。