MagicFace: High-Fidelity Facial Expression Editing with Action-Unit Control
This addresses the problem of realistic and controllable facial expression editing for applications in digital media and human-computer interaction, representing an incremental improvement with specific technical innovations.
The paper tackles facial expression editing by controlling facial action-unit variations to enable fine-grained, continuous, and interpretable editing while preserving identity, pose, and background. It introduces MagicFace, a diffusion model with an ID encoder and Attribute Controller, achieving superior high-fidelity results compared to other methods.
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.