  We present 3DHumanGAN, a 3D-aware generative adversarial network that
synthesizes photorealistic images of full-body humans with consistent
appearances under different view-angles and body-poses. To tackle the
representational and computational challenges in synthesizing the articulated
structure of human bodies, we propose a novel generator architecture in which a
2D convolutional backbone is modulated by a 3D pose mapping network. The 3D
pose mapping network is formulated as a renderable implicit function
conditioned on a posed 3D human mesh. This design has several merits: i) it
leverages the strength of 2D GANs to produce high-quality images; ii) it
generates consistent images under varying view-angles and poses; iii) the model
can incorporate the 3D human prior and enable pose conditioning. Project page:
https://3dhumangan.github.io/.
