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In this work, we propose a novel deep network architecture for fast video inpainting. Built upon an image-based encoder-decoder model, our framework is designed to collect and refine information from neighbor frames and synthesize still-unknown regions. At the same time, the output is enforced to be temporally consistent by a recurrent feedback and a temporal memory module. Compared with the state-of-the-art image inpainting algorithm, our method produces videos that are much more semantically correct and temporally smooth. In contrast to the prior video completion method which relies on time-consuming optimization, our method runs in near real-time while generating competitive video results. Finally, we applied our framework to video retargeting task, and obtain visually pleasing results.","url_abs":"https://arxiv.org/abs/1905.01639v1","url_pdf":"https://arxiv.org/pdf/1905.01639v1.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":"deep-video-inpainting","repo_url":"https://github.com/mcahny/Deep-Video-Inpainting","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-video-inpainting","repo_url":"https://github.com/89viper/Python-Deep_Video_Inpainting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"video-denoising","task_name":"Video Denoising"},{"task_slug":"video-inpainting","task_name":"Video Inpainting"},{"task_slug":"video-to-video-synthesis","task_name":"Video-to-Video Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-inpainting-on-davis","task":"Video Inpainting","dataset":"DAVIS","model":"VINet","rank_in_archive_order":7,"of":11,"metrics":{"Ewarp":"0.1785","PSNR":"28.96","SSIM":"0.9411","VFID":"0.199"},"uses_additional_data":false},{"leaderboard":"/sota/video-inpainting-on-youtube-vos","task":"Video Inpainting","dataset":"YouTube-VOS 2018","model":"VINet","rank_in_archive_order":9,"of":10,"metrics":{"Ewarp":"0.1490","PSNR":"29.20","SSIM":"0.9434","VFID":"0.072"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.01639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.01639"}},"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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