Fast Facial Image Analogy with Spatial Guidance

Abstract

This paper proposes a novel method for fast facial image analogy with spatial guidance. Given a facial image A and another one B in a different style (color, tone, or texture), we are allowed to render the face in A with style B to output a stylized facial image A′ in a timely manner. For traditional image analogy, such a process could take an unbearable time and considerable computing resources. In this paper, for the first time, we make it possible to do image analogy at a speed much faster than the state-of-the-art method. Specifically, we first extract deep image features using a VGG-19 encoder, and implement patch-match with the guidance of facial landmarks. Then Procrustes analysis is applied to accelerate the program as a coarse-to-fine strategy. We repeat the above process at each layer of VGG-19 and decode image A′ from bottom to top. Experimental results show that our method not only spends much less time but also provides high-quality image analogy results. Moreover, our method can be naturally extended to many face-related applications including but not limited to face swapping, makeup, and style to photo.