{"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/advanced-video-inpainting-using-optical-flow","title":"Advanced Video Inpainting Using Optical Flow-Guided Efficient Diffusion","arxiv_id":"2412.00857","date":"2024-12-01","proceeding":null,"authors":["Bohai Gu","Hao Luo","Song Guo","Peiran Dong"],"abstract":"Recently, diffusion-based methods have achieved great improvements in the video inpainting task. However, these methods still face many challenges, such as maintaining temporal consistency and the time-consuming issue. This paper proposes an advanced video inpainting framework using optical Flow-guided Efficient Diffusion, called FloED. Specifically, FloED employs a dual-branch architecture, where a flow branch first restores corrupted flow and a multi-scale flow adapter provides motion guidance to the main inpainting branch. Additionally, a training-free latent interpolation method is proposed to accelerate the multi-step denoising process using flow warping. Further introducing a flow attention cache mechanism, FLoED efficiently reduces the computational cost brought by incorporating optical flow. Comprehensive experiments in both background restoration and object removal tasks demonstrate that FloED outperforms state-of-the-art methods from the perspective of both performance and efficiency.","url_abs":"https://arxiv.org/abs/2412.00857v2","url_pdf":"https://arxiv.org/pdf/2412.00857v2.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":"advanced-video-inpainting-using-optical-flow","repo_url":"https://github.com/nevsnev/fgdvi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"video-inpainting","task_name":"Video Inpainting"}],"methods":[{"method_slug":"adapter","method_name":"Adapter"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"pixel-prediction","method_name":"Inpainting"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2412.00857","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}