Large Scale Image Completion via Co-Modulated Generative Adversarial NetworksDownload PDF

28 Sept 2020, 15:48 (edited 10 Feb 2022)ICLR 2021 SpotlightReaders: Everyone
  • Keywords: image completion, generative adversarial networks, co-modulation
  • 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.
  • One-sentence Summary: Bridging the gap between between image-conditional and unconditional GAN architectures via co-modulation
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  • Code: [![github](/images/github_icon.svg) zsyzzsoft/co-mod-gan](https://github.com/zsyzzsoft/co-mod-gan)
  • Data: [COCO-Stuff](https://paperswithcode.com/dataset/coco-stuff), [FFHQ](https://paperswithcode.com/dataset/ffhq), [Places](https://paperswithcode.com/dataset/places)
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