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

TL;DR

MagGAN is a novel high-resolution face editing method that uses facial masks to guide precise attribute modifications, achieving superior image quality and editing accuracy compared to previous approaches.

Contribution

Introduces a mask-guided approach with a reconstruction loss and conditioning strategy for precise, high-resolution face attribute editing.

Findings

Outperforms state-of-the-art in image quality

Effective at high-resolution (1024x1024) editing

Preserves irrelevant regions during editing

Abstract

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.