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3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping

ICCV 2023

Zhuoqian Yang1,2, Shikai Li1, Wayne Wu1† , Bo Dai1
1Shanghai AI Laboratory, 2EPFL
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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.

Video Demo

Pose Interpolation

Our 3D-aware GAN can be used to render simple animation of generated humans by interpolating between poses.


If you find this work useful for your research, please consider citing our paper:

  title={3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping},
  author={Yang, Zhuoqian and Li, Shikai and Wu, Wayne and Dai, Bo},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},