Content-Aware Latent Semantic Direction Fusion for Multi-Attribute Editing

Abstract

For facial attribute editing, significant progress has been made in discovering semantic directions in the latent space of StyleGAN, and the manipulation is performed by mapping an input image to a latent code and then moving along a direction associated with a target attribute. In this case, multi-attribute editing typically needs a sequential transformation process, which may cause ineffective manipulation or the cumulative effect on irrelevant attribute deviation. In this work, we aim to simultaneously manipulate multiple attributes through a single transformation. Toward this end, we propose a StyleGAN-based latent semantic direction fusion model, referred to as StyleLSF. There are two learnable components: a content-aware direction predictor learns to infer the latent directions, which are associated with preset attributes. A fusion network fuses the directions with respect to target attributes and yields a single translation vector. We further ensure irrelevant attribute preservation by imposing an attribute-aware feature consistency regularization approach.