Related papers: FaR-GAN for One-Shot Face Reenactment

FaR-GAN for One-Shot Face Reenactment

URL: http://arxiv.org/abs/2005.06402v1
Date: Wed, 13 May 2020 16:15:37 GMT
Title: FaR-GAN for One-Shot Face Reenactment
Authors: Hanxiang Hao and Sriram Baireddy and Amy R. Reibman and Edward J. Delp
Abstract summary: 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. The proposed method makes no assumptions about the source identity, facial expression, head pose, or even image background.
Score: 20.894596219099164
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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