来自今天的爱可可AI前沿推介

[CV] 3DHumanGAN: Towards Photo-Realistic 3D-Aware Human Image Generation

Z Yang, S Li, W Wu, B Dai
[Shanghai AI Lab & SenseTime Research]

3DHumanGAN: 逼真3D人体图像生成研究

要点：

  1. 提出3DHumanGAN，一种3D感知生成对抗网络(GAN)，用于合成逼真的全身人体图像；
  2. 提出一种新的2D-3D混合生成器体系结构，既高效又富有表现力；
  3. 采用基于分割的GAN损失进行监督，以建立3D坐标和2D人体语义之间的映射。

摘要：
本文提出3DHumanGAN，一种3D感知生成对抗网络(GAN)，可以合成在不同视角和身体姿态的具有一致外观的人体全身图像。为解决合成人体铰接结构的表征和计算挑战，本文提出一个新的生成器架构，其中2D卷积主干被3D姿态映射网络所调制。3D姿势映射网络被表述为一个可渲染的隐函数，以摆好的3D人体网格为条件。这种设计有几个优点：i）允许利用2D GAN的能力来生成逼真的图像；ii）在不同的视角和可指定的姿态下生成一致的图像；iii）可以从3D人体先验中受益。所提出模型是通过对抗学习从网络图像集中获得的，不需要人工标注。

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.

论文链接：https://arxiv.org/abs/2212.07378
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