Few-Shot Face-to-Face Translation and Swapping Using Synthesis Methods of GANs and Autoencoders
Tong Wang · 2025
Face swapping and face-to-face translation have recently garnered significant attention in computer vision and graphics, fueled by breakthroughs in generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). These technologies enable a variety of applications ranging from visual effects in movies and artistic style transfers to malicious misinformation campaigns. In this paper, we provide a detailed overview of few-shot face swapping and face-toface translation techniques using state-of-the-art synthesis methods that leverage both GANs and autoencoders. We offer a comprehensive problem outline, wherein we wish to replace the identity of a person in a single or few images (person A) with the identity of another person (person B) while preserving pose, facial expression, gaze direction, hairstyle, and overall illumination. We detail the underlying theory of autoencoders, GANs, and hybrid approaches such as VAE-GAN and UNIT models, discuss relevant literature on face synthesis and manipulation, present the concept of style transfer in the context of face swapping, and explain optimization-based as well as feed-forward approaches for generating photorealistic swapped faces. We also review important loss functions (content, style, total variation, etc.) and reference the powerful concept of poisson image editing for seamless image blending. We close by examining open challenges and discussing potential future directions for few-shot face swapping methods that balance quality, speed, and robustness to extreme pose or expression variations.