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从娱乐到专业领域的众多应用至关重要。近年来，可微渲染的进展使得无需3D真实数据即可训练3D生成模型成为可能。然而，生成的3D人物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人物框架，仅通过有限预算的3D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人物生成器与3D重建器习得的人物相关先验中获益。具体而言，我们通过一个专门设计的先验网络将Get3DHuman的潜在空间与StyleGAN-Human的潜在空间桥接，其中输入的潜在编码被映射为由像素对齐3D重建器构建的形状与纹理特征体。随后，先验网络的输出被用作主干生成器网络的监督信号。为确保有效训练，我们进一步提出了针对生成特征体与中间特征图的三项定制化损失函数。大量实验表明，Get3DHuman显著优于其他前沿方法，并能支持形状插值、形状重纹理以及通过潜在反演进行单视图重建等广泛应用。