Controllable face image editing in a disentanglement way

The prevalence of deep learning has attracted interest in the face image manipulation domain, especially in face editing with disentanglement representation. However, how to realize controllable disentangled representation of face images still remains challenging. Current methods require extensive supervision and training, or images will have a significantly impaired quality. We present an approach that learns how to represent data in an ideal disentangled way, with minimal supervision. Specifically, we use a swapping autoencoder with identity and attribute branches to learn identity and attribute representations, respectively. In addition, we separate the process of disentanglement and synthesis by an advanced pre-trained unsupervised StyleGAN2 image generator to make the entire network structure focus on learning data disentanglement. The identity and attribute vectors from different images are combined into a new representation that is mapped by a linear mapper into the generator's latent space to generate a new hybrid image. In this way, we take advantage of StyleGAN2's most advanced quality and its expressive latent space without the pressure of training a decoder. Experimental results prove that our method successfully separates the identity and other attributes of face images, outperforms existing methods, and requires less training and supervision. (c) 2023 SPIE and IS&T