SimSwap: An Efficient Framework For High Fidelity Face Swapping

11 Jun 2021  ·  Renwang Chen, Xuanhong Chen, Bingbing Ni, Yanhao Ge ·

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face... We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using this module, we extend the architecture of an identity-specific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art methods. The code is already available on github: https://github.com/neuralchen/SimSwap. read more

PDF Abstract

Results from the Paper


 Ranked #1 on Face Swapping on FaceForensics++ (ID retrieval metric)

     Get a GitHub badge
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Face Swapping FaceForensics++ SimSwap-oFM pose 1.22 # 2
Face Swapping FaceForensics++ SimSwap-nFM ID retrieval 96.57 # 1

Methods


No methods listed for this paper. Add relevant methods here