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：高质量3D数字人的快速生成对于从娱乐到专业领域的大量应用至关重要。近年来，可微渲染技术的进步使得无需三维真实数据即可训练3D生成模型成为可能。然而，所生成的3D人类模型在保真度和多样性方面仍有很大提升空间。本文提出Get3DHuman——一种新颖的3D人体框架，该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：3D生成器可从2D人体生成器和3D重建器学习到的人体相关先验中获益。具体而言，我们通过专门设计的先验网络将Get3DHuman的潜在空间与StyleGAN-Human的潜在空间相连接，输入潜在编码被映射到由像素对齐3D重建器生成的形状与纹理特征体。先验网络的输出随后被用作主生成网络的监督信号。为确保有效训练，我们进一步提出了三种针对生成特征体与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，并支持形状插值、形状重纹理、以及通过潜在反演实现单视图重建等多种应用。