HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
arXiv:2106.09965
Abstract
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.
arXiv commentAccepted to IJCAI 2021, project website: https://johann.wang/HifiFace
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
- High-Resolution Representations for Labeling Pixels and Regions
- FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
- The Deepfake Detection Challenge (DFDC) Preview Dataset
- FSNet: An Identity-Aware Generative Model for Image-based Face Swapping
- DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection