# FSGAN: Subject Agnostic Face Swapping and Reenactment

## TL;DR

FSGAN is a novel subject-agnostic face swapping and reenactment system that employs advanced neural networks for pose, expression, and occlusion handling, achieving superior results without requiring face-specific training.

## Contribution

The paper introduces a subject-agnostic face swapping framework with a new RNN-based reenactment, face completion, and blending networks, including a novel Poisson blending loss.

## Key findings

- Outperforms existing face swapping methods in quality.
- Handles pose, expression, and occlusion variations effectively.
- Achieves seamless blending with preserved skin tone and lighting.

## Abstract

We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces without requiring training on those faces. To this end, we describe a number of technical contributions. We derive a novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence. For video sequences, we introduce continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving target skin color and lighting conditions. This network uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior.