MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network
This addresses the problem of precise and high-resolution facial attribute editing for computer vision applications, representing an incremental improvement with novel conditioning and discriminator structures.
The paper tackled high-resolution face attribute editing by proposing MagGAN, which uses semantic facial masks to guide the editing process, resulting in significant outperformance over prior state-of-the-art methods on the CelebA benchmark in image quality and editing performance.
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.