论文标题
3Dhumangan：3D感知的人类形象生成3D姿势映射
3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping
论文作者
论文摘要
我们提出了3Dhumangan，这是一个3D感知的生成对抗网络，综合了全身人类的影像图像，并在不同的视角和身体姿势下具有一致的外观。为了应对综合人体铰接结构的代表性和计算挑战，我们提出了一种新颖的发电机体系结构，其中2D卷积骨架由3D姿势映射网络调节。 3D姿势映射网络被配制为以3D人类网格为条件的可渲染隐式函数。该设计具有多种优点：i）它利用2D甘斯的强度产生高质量的图像； ii) it generates consistent images under varying view-angles and poses; iii）该模型可以合并3D人类事先并实现姿势调节。项目页面：https：//3dhumangan.github.io/。
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/.
