{"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/rotmat2quat","entry":"rotmat2quat","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":5,"n_papers_ran":1,"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":3,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":2},"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":"2308.08942","paper":"/paper/auxiliary-tasks-benefit-3d-skeleton-based","title":"Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction","date":"2023-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mediabrain-sjtu/auxformer","path":"dataset/data_utils.py","file_url":"https://github.com/mediabrain-sjtu/auxformer/blob/HEAD/dataset/data_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a3ccebf0173ab84","mcp_get_code":{"code_sha256":"2a3ccebf0173ab84"}},{"arxiv_id":"1908.05436","paper":"/paper/learning-trajectory-dependencies-for-human","title":"Learning Trajectory Dependencies for Human Motion Prediction","date":"2019-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wei-mao-2019/LearnTrajDep","path":"utils/data_utils.py","file_url":"https://github.com/wei-mao-2019/LearnTrajDep/blob/HEAD/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf9baa714c29fd46","mcp_get_code":{"code_sha256":"bf9baa714c29fd46"}},{"arxiv_id":"1809.03036","paper":"/paper/a-neural-temporal-model-for-human-motion","title":"A Neural Temporal Model for Human Motion Prediction","date":"2018-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cr7anand/neural_temporal_models","path":"data_utils.py","file_url":"https://github.com/cr7anand/neural_temporal_models/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79f4ca59c2ff064b","mcp_get_code":{"code_sha256":"79f4ca59c2ff064b"}},{"arxiv_id":"1805.00655","paper":"/paper/convolutional-sequence-to-sequence-model-for","title":"Convolutional Sequence to Sequence Model for Human Dynamics","date":"2018-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics","path":"src/data_utils_cmu.py","file_url":"https://github.com/chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics/blob/HEAD/src/data_utils_cmu.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf9baa714c29fd46","mcp_get_code":{"code_sha256":"bf9baa714c29fd46"}},{"arxiv_id":"1805.00655","paper":"/paper/convolutional-sequence-to-sequence-model-for","title":"Convolutional Sequence to Sequence Model for Human Dynamics","date":"2018-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics","path":"src/data_utils.py","file_url":"https://github.com/chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics/blob/HEAD/src/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79f4ca59c2ff064b","mcp_get_code":{"code_sha256":"79f4ca59c2ff064b"}},{"arxiv_id":"136820244","paper":null,"title":"arXiv:136820244","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Sirui-Xu/STARS","path":"deterministic/utils/data_utils.py","file_url":"https://github.com/Sirui-Xu/STARS/blob/HEAD/deterministic/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf9baa714c29fd46","mcp_get_code":{"code_sha256":"bf9baa714c29fd46"}}]}