HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

Image credit: Unsplash


In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method. Meanwhile, we introduce the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic. Extensive experiments on faces in the wild demonstrate that our method can preserve better identity, especially on the face shape, and can generate more photo-realistic results than previous state-of-the-art methods.

In 2021 International Joint Conferences on Artificial Intelligence (IJCAI 2021)
Yuhan Wang
Yuhan Wang
Ph.D. student of MMLab@NTU

My research interests include AI Generation and Graph Machine Learning.