MU-GAN:Facial Attribute Editing Based on Multi-Attention Mechanism

Facial attribute editing has mainly two objectives:1)translating image from a source domain to a target one,and 2)only changing the facial regions related to a target attribute and preserving the attribute-excluding details.In this work,we propose a multi-attention U-Net-based generative adversarial network(MU-GAN).First,we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator,and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decoder with attribute-excluding detail and enhance attribute editing ability.Second,a self-attention(SA)mechanism is incorporated into convolutional layers for modeling long-range and multi-level dependencies across image regions.Experimental results indicate that our method is capable of balancing attribute editing ability and details preservation ability,and can decouple the correlation among attributes.It outperforms the state-of-the-art methods in terms of attribute manipulation accuracy and image quality.Our code is available at https://github.com/SuSir1996/MU-GAN.