{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/mpi-inf-3dhp/papers/ran/1","list_of":"/dataset/mpi-inf-3dhp","dataset":"MPI-INF-3DHP","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,25],"of":25,"counts":{"papers_with_a_benchmark_row":100,"with_a_code_link":71,"where_syntology_ran_a_sample":25,"not_listed_spam_title":0,"listed":100,"listed_where_code_ran":25,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":21,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":21,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/mpi-inf-3dhp/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/ktpformer-kinematics-and-trajectory-prior","slug":"ktpformer-kinematics-and-trajectory-prior","title":"KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation","date":"2024-03-31","arxiv_id":"2404.00658","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":10,"official":{"repos":["JihuaPeng/KTPFormer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ktpformer-kinematics-and-trajectory-prior#ran","syntology_url":"https://syntology.ai/paper/2404.00658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00658"}}}},{"paper":"/paper/motionagformer-enhancing-3d-human-pose","slug":"motionagformer-enhancing-3d-human-pose","title":"MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network","date":"2023-10-25","arxiv_id":"2310.16288","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":16,"samples_ran":14,"samples_constructed":0,"samples_ran_checked":13,"samples_ran_instrument_failed":1,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["taatiteam/motionagformer"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/motionagformer-enhancing-3d-human-pose#ran","syntology_url":"https://syntology.ai/paper/2310.16288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16288"}}}},{"paper":"/paper/hybrik-x-hybrid-analytical-neural-inverse","slug":"hybrik-x-hybrid-analytical-neural-inverse","title":"HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery","date":"2023-04-12","arxiv_id":"2304.05690","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":8,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hybrik-x-hybrid-analytical-neural-inverse#ran","syntology_url":"https://syntology.ai/paper/2304.05690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05690"}}}},{"paper":"/paper/poseformerv2-exploring-frequency-domain-for","slug":"poseformerv2-exploring-frequency-domain-for","title":"PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation","date":"2023-03-30","arxiv_id":"2303.17472","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":5,"samples_constructed":5,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["qitaozhao/poseformerv2"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/poseformerv2-exploring-frequency-domain-for#ran","syntology_url":"https://syntology.ai/paper/2303.17472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17472"}}}},{"paper":"/paper/ikol-inverse-kinematics-optimization-layer","slug":"ikol-inverse-kinematics-optimization-layer","title":"IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiation","date":"2023-02-02","arxiv_id":"2302.01058","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":7,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["juzezhang/ikol"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ikol-inverse-kinematics-optimization-layer#ran","syntology_url":"https://syntology.ai/paper/2302.01058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01058"}}}},{"paper":"/paper/gfpose-learning-3d-human-pose-prior-with","slug":"gfpose-learning-3d-human-pose-prior-with","title":"GFPose: Learning 3D Human Pose Prior with Gradient Fields","date":"2022-12-16","arxiv_id":"2212.08641","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["Embracing/GFPose"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/gfpose-learning-3d-human-pose-prior-with#ran","syntology_url":"https://syntology.ai/paper/2212.08641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08641"}}}},{"paper":"/paper/diffpose-toward-more-reliable-3d-pose","slug":"diffpose-toward-more-reliable-3d-pose","title":"DiffPose: Toward More Reliable 3D Pose Estimation","date":"2022-11-30","arxiv_id":"2211.16940","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":14,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":4,"samples_unverified":6,"pointer_only_for_licence":9,"official":{"repos":["GONGJIA0208/Diffpose"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/diffpose-toward-more-reliable-3d-pose#ran","syntology_url":"https://syntology.ai/paper/2211.16940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.16940"}}}},{"paper":"/paper/ray3d-ray-based-3d-human-pose-estimation-for","slug":"ray3d-ray-based-3d-human-pose-estimation-for","title":"Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization","date":"2022-03-22","arxiv_id":"2203.11471","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":4,"samples_constructed":4,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["YxZhxn/Ray3D"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/ray3d-ray-based-3d-human-pose-estimation-for#ran","syntology_url":"https://syntology.ai/paper/2203.11471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11471"}}}},{"paper":"/paper/p-stmo-pre-trained-spatial-temporal-many-to","slug":"p-stmo-pre-trained-spatial-temporal-many-to","title":"P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation","date":"2022-03-15","arxiv_id":"2203.07628","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":9,"samples_constructed":6,"samples_ran_checked":7,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["patrick-swk/p-stmo"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/p-stmo-pre-trained-spatial-temporal-many-to#ran","syntology_url":"https://syntology.ai/paper/2203.07628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07628"}}}},{"paper":"/paper/mixste-seq2seq-mixed-spatio-temporal-encoder","slug":"mixste-seq2seq-mixed-spatio-temporal-encoder","title":"MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video","date":"2022-03-02","arxiv_id":"2203.00859","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":4,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":4,"official":{"repos":["JinluZhang1126/MixSTE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/mixste-seq2seq-mixed-spatio-temporal-encoder#ran","syntology_url":"https://syntology.ai/paper/2203.00859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.00859"}}}},{"paper":"/paper/mhformer-multi-hypothesis-transformer-for-3d","slug":"mhformer-multi-hypothesis-transformer-for-3d","title":"MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation","date":"2021-11-24","arxiv_id":"2111.12707","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":8,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":1,"official":{"repos":["Vegetebird/MHFormer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/mhformer-multi-hypothesis-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2111.12707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.12707"}}}},{"paper":"/paper/learning-to-regress-bodies-from-images-using-1","slug":"learning-to-regress-bodies-from-images-using-1","title":"Learning to Regress Bodies from Images using Differentiable Semantic Rendering","date":"2021-10-07","arxiv_id":"2110.03480","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":3,"samples_unverified":0,"pointer_only_for_licence":3,"official":{"repos":["saidwivedi/DSR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-to-regress-bodies-from-images-using-1#ran","syntology_url":"https://syntology.ai/paper/2110.03480","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03480"}}}},{"paper":"/paper/encoder-decoder-with-multi-level-attention","slug":"encoder-decoder-with-multi-level-attention","title":"Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation","date":"2021-09-06","arxiv_id":"2109.02303","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":0,"samples_unverified":8,"pointer_only_for_licence":0,"official":{"repos":["ziniuwan/maed"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":8,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/encoder-decoder-with-multi-level-attention#ran","syntology_url":"https://syntology.ai/paper/2109.02303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02303"}}}},{"paper":"/paper/probabilistic-monocular-3d-human-pose","slug":"probabilistic-monocular-3d-human-pose","title":"Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows","date":"2021-07-29","arxiv_id":"2107.13788","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["twehrbein/Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/probabilistic-monocular-3d-human-pose#ran","syntology_url":"https://syntology.ai/paper/2107.13788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.13788"}}}},{"paper":"/paper/conditional-directed-graph-convolution-for-3d","slug":"conditional-directed-graph-convolution-for-3d","title":"Conditional Directed Graph Convolution for 3D Human Pose Estimation","date":"2021-07-16","arxiv_id":"2107.07797","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/conditional-directed-graph-convolution-for-3d#ran","syntology_url":"https://syntology.ai/paper/2107.07797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07797"}}}},{"paper":"/paper/graph-stacked-hourglass-networks-for-3d-human","slug":"graph-stacked-hourglass-networks-for-3d-human","title":"Graph Stacked Hourglass Networks for 3D Human Pose Estimation","date":"2021-03-30","arxiv_id":"2103.16385","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":15,"samples_ran":12,"samples_constructed":10,"samples_ran_checked":11,"samples_ran_instrument_failed":1,"samples_unverified":3,"pointer_only_for_licence":15,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/graph-stacked-hourglass-networks-for-3d-human#ran","syntology_url":"https://syntology.ai/paper/2103.16385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16385"}}}},{"paper":"/paper/context-modeling-in-3d-human-pose-estimation","slug":"context-modeling-in-3d-human-pose-estimation","title":"Context Modeling in 3D Human Pose Estimation: A Unified Perspective","date":"2021-03-29","arxiv_id":"2103.15507","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":1,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/context-modeling-in-3d-human-pose-estimation#ran","syntology_url":"https://syntology.ai/paper/2103.15507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15507"}}}},{"paper":"/paper/3d-human-pose-estimation-with-spatial-and","slug":"3d-human-pose-estimation-with-spatial-and","title":"3D Human Pose Estimation with Spatial and Temporal Transformers","date":"2021-03-18","arxiv_id":"2103.10455","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":4,"samples_constructed":2,"samples_ran_checked":4,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":8,"official":{"repos":["zczcwh/PoseFormer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/3d-human-pose-estimation-with-spatial-and#ran","syntology_url":"https://syntology.ai/paper/2103.10455","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.10455"}}}},{"paper":"/paper/hybrik-a-hybrid-analytical-neural-inverse","slug":"hybrik-a-hybrid-analytical-neural-inverse","title":"HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation","date":"2020-11-30","arxiv_id":"2011.14672","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["Jeff-sjtu/HybrIK"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hybrik-a-hybrid-analytical-neural-inverse#ran","syntology_url":"https://syntology.ai/paper/2011.14672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.14672"}}}},{"paper":"/paper/weakly-supervised-generative-network-for","slug":"weakly-supervised-generative-network-for","title":"Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses","date":"2020-08-13","arxiv_id":"2008.05770","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":9,"pointer_only_for_licence":0,"official":{"repos":["chaneyddtt/weakly-supervised-3d-pose-generator"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":9,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/weakly-supervised-generative-network-for#ran","syntology_url":"https://syntology.ai/paper/2008.05770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05770"}}}},{"paper":"/paper/srnet-improving-generalization-in-3d-human","slug":"srnet-improving-generalization-in-3d-human","title":"SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach","date":"2020-07-18","arxiv_id":"2007.09389","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":1,"official":{"repos":["ailingzengzzz/Split-and-Recombine-Net"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/srnet-improving-generalization-in-3d-human#ran","syntology_url":"https://syntology.ai/paper/2007.09389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09389"}}}},{"paper":"/paper/cascaded-deep-monocular-3d-human-pose-1","slug":"cascaded-deep-monocular-3d-human-pose-1","title":"Cascaded deep monocular 3D human pose estimation with evolutionary training data","date":"2020-06-14","arxiv_id":"2006.07778","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["Nicholasli1995/EvoSkeleton"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/cascaded-deep-monocular-3d-human-pose-1#ran","syntology_url":"https://syntology.ai/paper/2006.07778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07778"}}}},{"paper":"/paper/motion-guided-3d-pose-estimation-from-videos","slug":"motion-guided-3d-pose-estimation-from-videos","title":"Motion Guided 3D Pose Estimation from Videos","date":"2020-04-29","arxiv_id":"2004.13985","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/motion-guided-3d-pose-estimation-from-videos#ran","syntology_url":"https://syntology.ai/paper/2004.13985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13985"}}}},{"paper":"/paper/generating-multiple-hypotheses-for-3d-human","slug":"generating-multiple-hypotheses-for-3d-human","title":"Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network","date":"2019-04-11","arxiv_id":"1904.05547","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":1,"samples_unverified":5,"pointer_only_for_licence":0,"official":{"repos":["chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/generating-multiple-hypotheses-for-3d-human#ran","syntology_url":"https://syntology.ai/paper/1904.05547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.05547"}}}},{"paper":"/paper/single-shot-multi-person-3d-pose-estimation","slug":"single-shot-multi-person-3d-pose-estimation","title":"Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB","date":"2017-12-09","arxiv_id":"1712.03453","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":1,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/single-shot-multi-person-3d-pose-estimation#ran","syntology_url":"https://syntology.ai/paper/1712.03453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.03453"}}}}],"record_sha256":"a589b3679732af548ca9d24098137ce23d03e69077070134fbada7ed0faf059c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}