3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping
We present 3DHumanGAN, a 3D-aware generative adversarial network that synthesizes photo-like images of fullbody 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. Our model is adversarially learned from a collection of web images needless of manual annotation.
WOS:001169500507055
2023-01-01
979-8-3503-0718-4
Los Alamitos
22951
22962
REVIEWED
Event name | Event place | Event date |
Paris, FRANCE | OCT 02-06, 2023 | |