Lifting 2D StyleGAN for 3D-Aware Face Generation
arXiv:2011.13126
Abstract
We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation. Our model is "3D-aware" in the sense that it is able to (1) disentangle the latent space of StyleGAN2 into texture, shape, viewpoint, lighting and (2) generate 3D components for rendering synthetic images. Unlike most previous methods, our method is completely self-supervised, i.e. it neither requires any manual annotation nor 3DMM model for training. Instead, it learns to generate images as well as their 3D components by distilling the prior knowledge in StyleGAN2 with a differentiable renderer. The proposed model is able to output both the 3D shape and texture, allowing explicit pose and lighting control over generated images. Qualitative and quantitative results show the superiority of our approach over existing methods on 3D-controllable GANs in content controllability while generating realistic high quality images.
in CVPR 2021
References in corpus (5)
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Learning Face Representation from Scratch
- Accelerating 3D Deep Learning with PyTorch3D
- Unsupervised Generative 3D Shape Learning from Natural Images
- Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data