  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 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.
