High Fidelity and Real-time Video Face Swapping

Abstract

We present FaceFlowSwap (FFS), a video faceswapping method that achieves competitive quality with diffusion-based methods while meeting real-time constraints across video domains. FFS comprises a source-identity encoder, a video fusion module that aggregates cross-frame evidence via 3D convolutions and combines it with source-identity features, and a swapper that generates frames with a swapped face. Robustness to pose and motion is achieved through spatiotemporal adversarial learning combined with a source-identityenhancing loss, extended to the spatio-temporal dimension. Our ablations verify the contribution of each component to temporal coherence, controllability, and speed. Evaluated on FaceForensics++ and VFHQ datasets, FFS achieves state-of-theart identity preservation and visual quality while maintaining 21.7 ms processing speed per frame, which matches the real-time condition. © 2026 IEEE.