SSFlow: Style-guided Neural Spline Flows for Face Image Manipulation

Abstract

Significant progress has been made in high-resolution and photo-realistic image generation by Generative Adversarial Networks (GANs). However, the generation process is still lack of control, which is crucial for semantic face editing. Furthermore, it remains challenging to edit target attributes and preserve the identity at the same time. In this paper, we propose SSFlow to achieve identity-preserved semantic face manipulation in StyleGAN latent space based on conditional Neural Spline Flows. To further improve the performance of Neural Spline Flows on such task, we also propose Constractive Squash component and Blockwise 1 x 1 Convolution layer. Moreover, unlike other conditional flow-based approaches that require facial attribute labels during inference, our method can achieve label-free manipulation in a more flexible way. As a result, our methods are able to perform well-disentangled edits along various attributes, and generalize well for both real and artistic face image manipulation. Qualitative and quantitative evaluations show the advantages of our method for semantic face manipulation over state-of-the-art approaches.