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NF refers to the recent class of methods for the neural representation of complex 3D scenes that has seen widespread success and application across computer vision. CURE represents the video as a continuous function parameterized by a coordinate-based neural network, whose inputs are the spatiotemporal coordinates and outputs are the corresponding RGB values. CURE introduces a new architecture that conditions the neural network on the input frames for imposing space-time consistency in the synthesized video. This not only improves the final interpolation quality, but also enables CURE to learn a prior across multiple videos. Experimental evaluations show that CURE achieves the state-of-the-art performance on video interpolation on several benchmark datasets.","url_abs":"https://arxiv.org/abs/2203.00137v1","url_pdf":"https://arxiv.org/pdf/2203.00137v1.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":"learning-cross-video-neural-representations","repo_url":"https://github.com/wustl-cig/CURE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"video-frame-interpolation","task_name":"Video Frame Interpolation"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-frame-interpolation-on-msu-video-frame","task":"Video Frame Interpolation","dataset":"MSU Video Frame Interpolation","model":"CURE","rank_in_archive_order":12,"of":24,"metrics":{"LPIPS":"0.029","MS-SSIM":"0.946","PSNR":"28.01","SSIM":"0.920","VMAF":"67.07"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-nvidia-dynamic","task":"Video Frame Interpolation","dataset":"Nvidia Dynamic Scene","model":"CURE","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"36.24","SSIM":"0.9839"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-snu-film-easy","task":"Video Frame Interpolation","dataset":"SNU-FILM (easy)","model":"CURE","rank_in_archive_order":8,"of":8,"metrics":{"PSNR":"39.9","SSIM":"0.9910"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-snu-film-extreme","task":"Video Frame Interpolation","dataset":"SNU-FILM (extreme)","model":"CURE","rank_in_archive_order":7,"of":8,"metrics":{"PSNR":"25.44","SSIM":"0.8638"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-snu-film-hard","task":"Video Frame Interpolation","dataset":"SNU-FILM (hard)","model":"CURE","rank_in_archive_order":7,"of":8,"metrics":{"PSNR":"30.66","SSIM":"0.9373"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-snu-film-medium","task":"Video Frame Interpolation","dataset":"SNU-FILM (medium)","model":"CURE","rank_in_archive_order":8,"of":8,"metrics":{"PSNR":"35.94","SSIM":"0.9797"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-ucf101-1","task":"Video Frame Interpolation","dataset":"UCF101","model":"CURE","rank_in_archive_order":10,"of":19,"metrics":{"PSNR":"35.36","SSIM":"0.9705"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-vimeo90k","task":"Video Frame Interpolation","dataset":"Vimeo90K","model":"CURE","rank_in_archive_order":11,"of":23,"metrics":{"PSNR":"35.73","SSIM":"0.9789"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-x4k1000fps-2k","task":"Video Frame Interpolation","dataset":"X4K1000FPS-2K","model":"CURE","rank_in_archive_order":4,"of":4,"metrics":{"PSNR":"30.05","SSIM":"0.8998"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-xiph-4k","task":"Video Frame Interpolation","dataset":"Xiph 4k","model":"CURE","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"30.94","SSIM":"0.9389"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.00137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.00137"}},"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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