Papers › GANimation: Anatomically-aware Facial Animation from a Single Image

GANimation: Anatomically-aware Facial Animation from a Single Image

24 Jul 2018ECCV 2018 9arXiv:1807.09251archive 2025-07-28

Albert Pumarola, Antonio Agudo, Aleix M. Martinez, Alberto Sanfeliu, Francesc Moreno-Noguer

Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis. The most successful architecture is StarGAN, that conditions GANs generation process with images of a specific domain, namely a set of images of persons sharing the same expression. While effective, this approach can only generate a discrete number of expressions, determined by the content of the dataset. To address this limitation, in this paper, we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describes in a continuous manifold the anatomical facial movements defining a human expression. Our approach allows controlling the magnitude of activation of each AU and combine several of them. Additionally, we propose a fully unsupervised strategy to train the model, that only requires images annotated with their activated AUs, and exploit attention mechanisms that make our network robust to changing backgrounds and lighting conditions. Extensive evaluation show that our approach goes beyond competing conditional generators both in the capability to synthesize a much wider range of expressions ruled by anatomically feasible muscle movements, as in the capacity of dealing with images in the wild.

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albertpumarola/GANimation officialmentioned on GitHubpytorchGPL-3.0 report
donydchen/ganimation_replicate mentioned on GitHubpytorchMIT report
hao-qiang/GANimation-tf mentioned on GitHubtf report
shuliyao/GANimation_TL mentioned on GitHubtf report
tgeorgy/mgan mentioned on GitHubpytorchMIT report
vipermu/ganimation mentioned on GitHubpytorch report
vpsg-research/waveguard mentioned on GitHubpytorch report

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Conditional Image GenerationFace GenerationImage-to-Image Translation

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Convolution

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