IA-FaceS: A bidirectional method for semantic face editing

Semantic face editing has achieved substantial progress in recent years. However, existing face editing methods, which often encode the entire image into a single code, still have difficulty in enabling flexible editing while keeping high-fidelity reconstruction. The one-code scheme also brings entangled face manipulations and limited flexibility in editing face components. In this paper, we present IA-FaceS, a bidirectional method for disentangled face attribute manipulation as well as flexible, controllable component editing. We propose to embed images onto two branches: one branch computes high-dimensional component-invariant content embedding for capturing face details, and the other provides low-dimensional component-specific embeddings for component manipulations. The two-branch scheme naturally enables high-quality facial component-level editing while keeping faithful reconstruction with details. Moreover, we devise a component adaptive modulation (CAM) module, which integrates component-specific guidance into the decoder and successfully disentangles highly-correlated face components. The single-eye editing is developed for the first time without editing face masks or sketches. According to the experimental results, IA-FaceS establishes a good balance between maintaining image details and performing flexible face manipulation. Both quanti-tative and qualitative results indicate that the proposed method outperforms the existing methods in reconstruction, face attribute manipulation, and component transfer. We release the code and weights at: https://github.com/CMACH508/IA-FaceS.(c) 2022 Elsevier Ltd. All rights reserved.