Composite makeup transfer model based on generative adversarial networks

Composite makeup transfer model based on generative adversarial networks

연구 분야: Artificial Intelligence

학회: Multimedia Systems

Existing methods for makeup transfer mainly focus on the transfer of facial makeup, while overlooking the importance of hairstyles. To address this issue, we propose a composite makeup transfer model based on generative adversarial networks, which cleverly achieves composite transfer of facial makeup and hairstyles. Our model consists of two parallel branches: the facial makeup transfer branch and the hairstyle transfer branch. The facial makeup transfer branch integrates semantic correspondence learning and utilizes a bidirectional semantic correspondence feature transfer module to model and promote accurate semantic correspondence. During the generation process, we spatially distort the extracted makeup features to achieve semantic alignment with the target image. Subsequently, we fuse the distorted makeup features with the unmodified makeup-irrelevant features to generate facial makeup transfer results. In the hairstyle transfer branch, we introduce a occlusion repair module. This module leverages semantic relationships to repair facial occlusions in the source image. Then, combined with the pose consistency module, it generates hairstyle transfer results. Finally, the overall model’s generation effect is regulated through the comprehensive loss function we propose. Experimental results demonstrate that our method achieves visually accurate composite makeup and hairstyle transfer results.