AmazingFS: A High-Fidelity and Occlusion-Resistant Video Face-Swapping Framework

Abstract

Current video face-swapping technologies face challenges such as poor facial fitting and the inability to handle obstructions. This paper introduces Amazing FaceSwap (AmazingFS), a novel framework for producing cinematic quality and realistic face swaps. Key innovations include the development of a Source-Target Attention Mechanism (STAM) to improve face-swap quality while preserving target face expressions and poses. We also enhanced the AdaIN style transfer module to better retain the identity features of the source face. To address obstructions like hair and glasses during face-swap synthesis, we created the AmazingSeg network and a small dataset AST. Extensive qualitative and quantitative experiments demonstrate that AmazingFS significantly outperforms other SOTA networks, achieving amazing face swap results.