{"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/blind-video-temporal-consistency-via-deep","title":"Blind Video Temporal Consistency via Deep Video Prior","arxiv_id":"2010.11838","date":"2020-10-22","proceeding":"NeurIPS 2020 12","authors":["Chenyang Lei","Yazhou Xing","Qifeng Chen"],"abstract":"Applying image processing algorithms independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previous methods that enforce temporal consistency with optical flow, we show that temporal consistency can be achieved by training a convolutional network on a video with the Deep Video Prior. Moreover, a carefully designed iteratively reweighted training strategy is proposed to address the challenging multimodal inconsistency problem. We demonstrate the effectiveness of our approach on 7 computer vision tasks on videos. Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency. Our source codes are publicly available at github.com/ChenyangLEI/deep-video-prior.","url_abs":"https://arxiv.org/abs/2010.11838v1","url_pdf":"https://arxiv.org/pdf/2010.11838v1.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":"blind-video-temporal-consistency-via-deep","repo_url":"https://github.com/ChenyangLEI/deep-video-prior","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"blind-video-temporal-consistency-via-deep","repo_url":"https://github.com/yzxing87/pytorch-deep-video-prior","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"video-temporal-consistency","task_name":"Video Temporal Consistency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2010.11838","atlas_url":"https://app.syntology.ai/?focus=2010.11838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11838"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/ChenyangLEI/deep-video-prior","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yzxing87/pytorch-deep-video-prior","reach":null}],"summary":{"ran":1,"ran_draft_wrong":1,"ran_honours":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"f7cfa746be654bcc","entry":"UNet","repo":"yzxing87/pytorch-deep-video-prior","repo_kind":"official","path":"models/network.py","file_url":"https://github.com/yzxing87/pytorch-deep-video-prior/blob/HEAD/models/network.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f7cfa746be654bcc"}},{"code_sha256_prefix":"b01ca069cf08abf6","entry":"compute_error","repo":"yzxing87/pytorch-deep-video-prior","repo_kind":"official","path":"main_IRT.py","file_url":"https://github.com/yzxing87/pytorch-deep-video-prior/blob/HEAD/main_IRT.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b01ca069cf08abf6"}},{"code_sha256_prefix":"38727b82d086e71a","entry":"prepare_paired_input","repo":"yzxing87/pytorch-deep-video-prior","repo_kind":"official","path":"main_IRT.py","file_url":"https://github.com/yzxing87/pytorch-deep-video-prior/blob/HEAD/main_IRT.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"38727b82d086e71a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}