Disentangled Feature Networks for Facial Portraits Generation

Abstract

Facial portrait is an artistic form which draws faces by emphasizing discriminative or prominent parts of faces via various kinds of drawing tools. However, the complex interplay between the different facial factors, such as facial parts, background, and drawing styles, and the significant domain gap between natural facial images and their portrait counterparts makes the task challenging. In this paper, a flexible four-stream Disentangled Feature Networks (DFN) is proposed to learn disentangled feature representation of different facial factors and generate plausible portraits with reasonable exaggerations and richness in style. Four factors are encoded as embedding features, and combined to reconstruct facial portraits. Meanwhile, to make the process fully automatic (without manually specifying either portrait style or exaggerating form), we propose a new Adversarial Portrait Mapping Module (APMM) to map noise to the embedding feature space, as proxies for portrait style and exaggerating. Thanks to the proposed DFN and APMM, we are able to manipulate the portrait style and facial geometric structures to generate a large number of portraits. Extensive experiments on two public datasets show that our proposed methods can generate a diverse set of artistic portraits.