MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network

Understand

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.