  We present Mask-guided Generative Adversarial Network (MagGAN) for
high-resolution face attribute editing, in which semantic facial masks from a
pre-trained face parser are used to guide the fine-grained image editing
process. With the introduction of a mask-guided reconstruction loss, MagGAN
learns to only edit the facial parts that are relevant to the desired attribute
changes, while preserving the attribute-irrelevant regions (e.g., hat, scarf
for modification `To Bald'). Further, a novel mask-guided conditioning strategy
is introduced to incorporate the influence region of each attribute change into
the generator. In addition, a multi-level patch-wise discriminator structure is
proposed to scale our model for high-resolution ($1024 \times 1024$) face
editing. Experiments on the CelebA benchmark show that the proposed method
significantly outperforms prior state-of-the-art approaches in terms of both
image quality and editing performance.
