{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wide-context-semantic-image-extrapolation","title":"Wide-Context Semantic Image Extrapolation","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Yi Wang"," Xin Tao"," Xiaoyong Shen"," Jiaya Jia"],"abstract":"This paper studies the fundamental problem of extrapolating visual context using deep generative models, i.e., extending image borders with plausible structure and details. This seemingly easy task actually faces many crucial technical challenges and has its unique properties. The two major issues are size expansion and one-side constraints. We propose a semantic regeneration network with several special contributions and use multiple spatial related losses to address these issues. Our results contain consistent structures and high-quality textures. Extensive experiments are conducted on various possible alternatives and related methods. We also explore the potential of our method for various interesting applications that can benefit research in a variety of fields.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Wide-Context_Semantic_Image_Extrapolation_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Wide-Context_Semantic_Image_Extrapolation_CVPR_2019_paper.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":"wide-context-semantic-image-extrapolation","repo_url":"https://github.com/shepnerd/outpainting_srn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"wide-context-semantic-image-extrapolation","repo_url":"https://github.com/zcemycl/Pytorch_Outpainting_SRN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"image-outpainting","task_name":"Image Outpainting"},{"task_slug":"seeing-beyond-the-visible","task_name":"Seeing Beyond the Visible"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/seeing-beyond-the-visible-on-kitti360-ex","task":"Seeing Beyond the Visible","dataset":"KITTI360-EX","model":"SRN","rank_in_archive_order":7,"of":7,"metrics":{"Average PSNR":"16.10"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}