We present 3DHumanGAN, a 3D-aware generative adversarial network (GAN) that
synthesizes 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 allows us to harness the power of
2D GANs to generate photo-realistic images; ii) it generates consistent images
under varying view-angles and specifiable poses; iii) the model can benefit
from the 3D human prior. Our model is adversarially learned from a collection
of web images needless of manual annotation.

本文提出了一种名为 3DHumanGAN 的生成对抗网络（GAN），用于合成全身人体的图像，具有在不同视角和姿势下一致的外观。该模型采用了 2D 卷积背骨由 3D 姿态映射网络调制的生成器架构，能够生成具有 3D 人体先验知识和一致性的逼真图像，并通过对抗学习从网络图片中学习而来。