GANimation: Anatomically-aware Facial Animation from a Single Image
This work addresses the problem of generating realistic and diverse facial animations from single images for applications in computer vision and graphics, representing a novel method for a known bottleneck.
The paper tackles the limitation of discrete facial expression synthesis in GANs by introducing a GAN conditioning scheme based on Action Units (AU) annotations, enabling continuous control over anatomically feasible expressions and robust handling of images in the wild.
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.