Controlling Facial Attribute Synthesis by Disentangling Attribute Feature Axes in Latent Space

Abstract

In this study, we propose a novel approach to synthesize high-resolution and hyper-realistic face images with controlled attributes. Firstly, by training an attribute classifier to assign attribute labels to given synthesized face images, we build the links between latent vectors and face attributes. Secondly, we adapt the regression method to match the distributions of latent vectors with the corresponding face attributes, to control the attribute synthesis in the face images. Finally, we use the Gram-Schmidt orthogonalization algorithm to disentangle the attribute feature axes in latent space, such that a change in one attribute will not cause any changes in other attributes. Extensive experiments demonstrate the effectiveness of the proposed approach for high-quality face image synthesis with controlled attributes.

Qiyu Wei 魏启宇

Ph.D student.

My research interests include AI in Semiconductor, 3D Deep Learning and Bayesian Optimisation.