Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

• URL: http://arxiv.org/abs/2103.10428v1
• Date: Thu, 18 Mar 2021 17:59:11 GMT
• Title: Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
• Authors: Shengyu Zhao, Jonathan Cui, Yilun Sheng, Yue Dong, Xiao Liang, Eric I Chang, Yan Xu
• Abstract summary: We propose a generic new approach that bridges the gap between image-conditional and recent unconditional generative architectures. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS) Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation.
• Score: 18.312552957727828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the perceptual fidelity of inpainted images compared to real images via linear separability in a feature space. Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan.

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