{"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/video-frame-interpolation-via-adaptive-1","title":"Video Frame Interpolation via Adaptive Convolution","arxiv_id":"1703.07514","date":"2017-03-22","proceeding":"CVPR 2017 7","authors":["Simon Niklaus","Long Mai","Feng Liu"],"abstract":"Video frame interpolation typically involves two steps: motion estimation and\npixel synthesis. Such a two-step approach heavily depends on the quality of\nmotion estimation. This paper presents a robust video frame interpolation\nmethod that combines these two steps into a single process. Specifically, our\nmethod considers pixel synthesis for the interpolated frame as local\nconvolution over two input frames. The convolution kernel captures both the\nlocal motion between the input frames and the coefficients for pixel synthesis.\nOur method employs a deep fully convolutional neural network to estimate a\nspatially-adaptive convolution kernel for each pixel. This deep neural network\ncan be directly trained end to end using widely available video data without\nany difficult-to-obtain ground-truth data like optical flow. Our experiments\nshow that the formulation of video interpolation as a single convolution\nprocess allows our method to gracefully handle challenges like occlusion, blur,\nand abrupt brightness change and enables high-quality video frame\ninterpolation.","url_abs":"http://arxiv.org/abs/1703.07514v1","url_pdf":"http://arxiv.org/pdf/1703.07514v1.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":"video-frame-interpolation-via-adaptive-1","repo_url":"https://github.com/gurpreet-singh135/Image-Interpolation-via-adaptive-separable-convolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"video-frame-interpolation","task_name":"Video Frame Interpolation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1703.07514","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}