Papers › Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning

Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning

24 Apr 2020CVPR 2020 6arXiv:2004.11660archive 2025-07-28

Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, Xin Tong

We propose DiscoFaceGAN, an approach for face image generation of virtual people with disentangled, precisely-controllable latent representations for identity of non-existing people, expression, pose, and illumination. We embed 3D priors into adversarial learning and train the network to imitate the image formation of an analytic 3D face deformation and rendering process. To deal with the generation freedom induced by the domain gap between real and rendered faces, we further introduce contrastive learning to promote disentanglement by comparing pairs of generated images. Experiments show that through our imitative-contrastive learning, the factor variations are very well disentangled and the properties of a generated face can be precisely controlled. We also analyze the learned latent space and present several meaningful properties supporting factor disentanglement. Our method can also be used to embed real images into the disentangled latent space. We hope our method could provide new understandings of the relationship between physical properties and deep image synthesis.

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microsoft/DisentangledFaceGAN officialmentioned in papermentioned on GitHubtfMIT report
microsoft/DiscoFaceGAN mentioned on GitHubtfMIT report
mk-minchul/cse802_face_augmentation mentioned on GitHubpytorch report
pengfudan/DisentangledFaceGAN mentioned on GitHubtfMIT report

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CoeffDecoder microsoft/DisentangledFaceGAN/generate_images.py official repository unverified MIT (permissive) · b841315157ec8c2b · report
FaceParser microsoft/DisentangledFaceGAN/training/networks_parser.py official repository unverified MIT (permissive) · e2e11f3a3cab6434 · report
G_wgan microsoft/DisentangledFaceGAN/training/loss.py official repository unverified MIT (permissive) · 07f42dc7a77da207 · report
MaskNet microsoft/DisentangledFaceGAN/training/networks_parser.py official repository unverified MIT (permissive) · 44b7510fbf57e6c6 · report
ask_yes_no microsoft/DisentangledFaceGAN/dnnlib/util.py official repository unverified MIT (permissive) · 9d31d2c4cd16bb2d · report
format_time microsoft/DisentangledFaceGAN/dnnlib/util.py official repository unverified MIT (permissive) · 053fc534bc6bb989 · report
fpn microsoft/DisentangledFaceGAN/training/networks_parser.py official repository unverified MIT (permissive) · 4192aa298eb02e26 · report
gaussian_blur microsoft/DisentangledFaceGAN/training/loss_control.py official repository unverified MIT (permissive) · 00e0c8745f3f99e9 · report
gaussian_kernel microsoft/DisentangledFaceGAN/training/loss_control.py official repository unverified MIT (permissive) · 66f866bbf81a287d · report
z_to_lambda_mapping microsoft/DisentangledFaceGAN/generate_images.py official repository unverified MIT (permissive) · 50b51702f15ad7d4 · report
tuple_product microsoft/DiscoFaceGAN/dnnlib/util.py community (archive-listed) ran fingerprinted MIT (permissive) · efd5d4ab788539f4 · report
execute_cmdline microsoft/DiscoFaceGAN/dataset_tool.py community (archive-listed) unverified MIT (permissive) · 31857d2f579c1600 · report

Tasks

Contrastive LearningDisentanglementImage Generation

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