MagicFace: High-Fidelity Facial Expression Editing with Action-Unit
  Control
Authors: name: Mengting Wei, name: Tuomas Varanka, name: Xingxun Jiang, name: Huai-Qian Khor, name: Guoying Zhao

We address the problem of facial expression editing by controling the
relative variation of facial action-unit (AU) from the same person. This
enables us to edit this specific person's expression in a fine-grained,
continuous and interpretable manner, while preserving their identity, pose,
background and detailed facial attributes. Key to our model, which we dub
MagicFace, is a diffusion model conditioned on AU variations and an ID encoder
to preserve facial details of high consistency. Specifically, to preserve the
facial details with the input identity, we leverage the power of pretrained
Stable-Diffusion models and design an ID encoder to merge appearance features
through self-attention. To keep background and pose consistency, we introduce
an efficient Attribute Controller by explicitly informing the model of current
background and pose of the target. By injecting AU variations into a denoising
UNet, our model can animate arbitrary identities with various AU combinations,
yielding superior results in high-fidelity expression editing compared to other
facial expression editing works. Code is publicly available at
https://github.com/weimengting/MagicFace.