{"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":"/code/flow-uv-to-colors","entry":"flow_uv_to_colors","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":8,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":1},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2407.14126","paper":"/paper/mono-vifi-a-unified-learning-framework-for","title":"Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujf1226/mono-vifi","path":"flow_vis.py","file_url":"https://github.com/liujf1226/mono-vifi/blob/HEAD/flow_vis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}},{"arxiv_id":"2407.09059","paper":"/paper/domain-adaptive-video-deblurring-via-test","title":"Domain-adaptive Video Deblurring via Test-time Blurring","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jin-ting-he/dadeblur","path":"BlurringModel/ID_Blau/flow_viz.py","file_url":"https://github.com/jin-ting-he/dadeblur/blob/HEAD/BlurringModel/ID_Blau/flow_viz.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}},{"arxiv_id":"2403.10362","paper":"/paper/cpga-coding-priors-guided-aggregation-network","title":"CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VQE-CPGA/CPGA","path":"utils/flow_viz.py","file_url":"https://github.com/VQE-CPGA/CPGA/blob/HEAD/utils/flow_viz.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}},{"arxiv_id":"2207.10314","paper":"/paper/semi-supervised-learning-of-optical-flow-by","title":"Semi-Supervised Learning of Optical Flow by Flow Supervisor","date":"2022-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iwbn/flow-supervisor","path":"pytorch/core/utils/flow_viz.py","file_url":"https://github.com/iwbn/flow-supervisor/blob/HEAD/pytorch/core/utils/flow_viz.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}},{"arxiv_id":"2203.00137","paper":"/paper/learning-cross-video-neural-representations","title":"Learning Cross-Video Neural Representations for High-Quality Frame Interpolation","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wustl-cig/CURE","path":"models/raftcore/utils/flow_viz.py","file_url":"https://github.com/wustl-cig/CURE/blob/HEAD/models/raftcore/utils/flow_viz.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}},{"arxiv_id":"2008.01484","paper":"/paper/learning-stereo-from-single-images","title":"Learning Stereo from Single Images","date":"2020-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mattpoggi/depthstillation","path":"flow_colors.py","file_url":"https://github.com/mattpoggi/depthstillation/blob/HEAD/flow_colors.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"74db13ac0c032f32","mcp_get_code":{"code_sha256":"74db13ac0c032f32"}},{"arxiv_id":"Luan_Lifting_the_Structural_Morphing_for_Wide-Angle_Images_Rectification_Unified_Content_ICCV_2025_paper","paper":null,"title":"arXiv:Luan_Lifting_the_Structural_Morphing_for_Wide-Angle_Images_Rectification_Unified_Content_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lwttttt/ConBo-Net","path":"utils/flow_viz.py","file_url":"https://github.com/lwttttt/ConBo-Net/blob/HEAD/utils/flow_viz.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}},{"arxiv_id":"Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper","paper":null,"title":"arXiv:Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MakiseChris666/PixelStitch","path":"modules/flow_viz.py","file_url":"https://github.com/MakiseChris666/PixelStitch/blob/HEAD/modules/flow_viz.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae9446901af6b9a8","mcp_get_code":{"code_sha256":"ae9446901af6b9a8"}}]}