A novel method for facial attribute editing by integrating semantic segmentation and color rendering

Recently, facial attribute editing has been widely used in human-computer interaction and entertainment social fields. However, most existing facial attribute editing methods have some limitations such as low segmentation granularity and inability to accurately edit regions. To overcome the problems, the Semantic Rendering Generative Adversarial Networks which combines semantic segmentation and color rendering for facial attribute editing is presented. Firstly, asemantic segmentation network, which has limited operations to the target area due to without modifying any attribute-unrelated details, was constructed to generate masks of attribute-related regions. Secondly, to effectively generate color masks for synthesizing higher-quality images, a color rendering network model was derived by merging Transformer-based UNet encoder and ColorMapGAN decoder as the generator of the color rendering network. To verify the effectiveness of the proposed method, the constructed models had been trained on CelebA and CelebAMask-HQ datasets The experimental results shown that the proposed method can not only finely segment attribute-related and unrelated areas but also generate more realistic face images, compared with several existing facial attribute editing methods.