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Existing methods which\nextract information from only a single image generally produce unsatisfactory\nresults due to the lack of high level context. In this paper, we propose a\nnovel method for semantic image inpainting, which generates the missing content\nby conditioning on the available data. Given a trained generative model, we\nsearch for the closest encoding of the corrupted image in the latent image\nmanifold using our context and prior losses. This encoding is then passed\nthrough the generative model to infer the missing content. In our method,\ninference is possible irrespective of how the missing content is structured,\nwhile the state-of-the-art learning based method requires specific information\nabout the holes in the training phase. Experiments on three datasets show that\nour method successfully predicts information in large missing regions and\nachieves pixel-level photorealism, significantly outperforming the\nstate-of-the-art methods.","url_abs":"http://arxiv.org/abs/1607.07539v3","url_pdf":"http://arxiv.org/pdf/1607.07539v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/InternityFoundation/SemanticInpainting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/OlivierLessard/Semantic-Image-Inpainting-with-Deep-Generative-Models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/ahmedabdel-hady/Space-Xplores-Nasaspaceapp2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/bamos/dcgan-completion.tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/kok202/ApolloGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/moodoki/semantic_image_inpainting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"semantic-image-inpainting-with-deep","repo_url":"https://github.com/ntakouris/semantic-image-inpainting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.07539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.07539"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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