FaR-GAN for One-Shot Face Reenactment
This addresses the challenge of face reenactment for image editing and movie production by enabling one-shot generation without large datasets, though it is incremental as it builds on existing GAN-based approaches.
The paper tackles the problem of animating a static face image with target expressions using only one source image, presenting FaR-GAN which generates higher quality face images than compared methods on the VoxCeleb1 dataset.
Animating a static face image with target facial expressions and movements is important in the area of image editing and movie production. This face reenactment process is challenging due to the complex geometry and movement of human faces. Previous work usually requires a large set of images from the same person to model the appearance. In this paper, we present a one-shot face reenactment model, FaR-GAN, that takes only one face image of any given source identity and a target expression as input, and then produces a face image of the same source identity but with the target expression. The proposed method makes no assumptions about the source identity, facial expression, head pose, or even image background. We evaluate our method on the VoxCeleb1 dataset and show that our method is able to generate a higher quality face image than the compared methods.