  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.
