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

• URL: http://arxiv.org/abs/2010.01424v1
• Date: Sat, 3 Oct 2020 20:56:16 GMT
• Title: MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network
• Authors: Yi Wei, Zhe Gan, Wenbo Li, Siwei Lyu, Ming-Ching Chang, Lei Zhang, Jianfeng Gao, Pengchuan Zhang
• Abstract summary: MagGAN learns to only edit the facial parts that are relevant to the desired attribute changes. A novel mask-guided conditioning strategy is introduced to incorporate the influence region of each attribute change into the generator. A multi-level patch-wise discriminator structure is proposed to scale our model for high-resolution ($1024 times 1024$) face editing.
• Score: 145.4591079418917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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