FaR-GAN for One-Shot Face Reenactment

Abstract

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.

arXiv commentThis paper has been accepted to the AI for content creation workshop at CVPR 2020

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Cited by in corpus (3)

• Deep Learning Based Single Sample Per Person Face Recognition: A Survey
• Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis
• Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation