FaceShapeGene: A disentangled shape representation for flexible face image editing

Abstract

How do I look if I have the same nose shape as my favorite star? Existing methods for face image manipulation generally focus on modifying predefined facial attributes, editing expressions and changing image styles, where users cannot control the shapes of specific semantic facial parts freely in the generated face image. The facial part shapes are described by their geometries and need to be controlled by continuously generating geometric parameters. Therefore, the existing methods that work with discretely labelled attributes are not applicable on this task. In this paper, we propose a novel approach to learn the disentangled shape representation for a face image, namely the FaceShapeGene, which encodes the shape information of the semantic facial parts into separate chunks in the latent space. It allows users to freely recombine the part-wise latent chunks of a face image from other individuals to transfer a specified facial part shape, just like gene editing. Experimental results on several tasks demonstrate that the proposed FaceShapeGene representation correctly disentangles the shape features of different semantic parts. Comparisons to existing methods show the superiority of the proposed method on facial parts editing tasks.