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Rather than filling in the RGB pixels of each frame directly, we consider video inpainting as a pixel propagation problem. We first synthesize a spatially and temporally coherent optical flow field across video frames using a newly designed Deep Flow Completion network. Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video. Specifically, the Deep Flow Completion network follows a coarse-to-fine refinement to complete the flow fields, while their quality is further improved by hard flow example mining. Following the guide of the completed flow, the missing video regions can be filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of inpainting quality and speed.","url_abs":"https://arxiv.org/abs/1905.02884v1","url_pdf":"https://arxiv.org/pdf/1905.02884v1.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-flow-guided-video-inpainting","repo_url":"https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deep-flow-guided-video-inpainting","repo_url":"https://github.com/Lubsey1/inpainting_colab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"one-shot-visual-object-segmentation","task_name":"One-shot visual object segmentation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"video-inpainting","task_name":"Video Inpainting"},{"task_slug":null,"task_name":"Video fixed region Inpainting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-inpainting-on-davis","task":"Video Inpainting","dataset":"DAVIS","model":"DFVI","rank_in_archive_order":8,"of":11,"metrics":{"Ewarp":"0.1608","PSNR":"28.81","SSIM":"0.9404","VFID":"0.187"},"uses_additional_data":false},{"leaderboard":"/sota/video-inpainting-on-youtube-vos","task":"Video Inpainting","dataset":"YouTube-VOS 2018","model":"DFVI","rank_in_archive_order":10,"of":10,"metrics":{"Ewarp":"0.1509","PSNR":"29.16","SSIM":"0.9429","VFID":"0.066"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1905.02884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.02884"}},"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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