{"url":"/sota/pose-estimation-on-salsa","task":{"name":"Pose Estimation","url":"/task/pose-estimation","note":null},"dataset":{"name":"SALSA","url":"/dataset/salsa"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Pose Estimation** is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.\r\n\r\nA common benchmark for this task is [MPII Human Pose](https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose)\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":4,"rows_with_code":4,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SubdivNet","metrics":{"Accuracy":"93"},"uses_additional_data":false,"paper_date":"2021-06-04","paper":"/paper/subdivision-based-mesh-convolution-networks","paper_url":"https://arxiv.org/abs/2106.02285v2","paper_title":"Subdivision-Based Mesh Convolution Networks","code":"https://github.com/lzhengning/SubdivNet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"MeshCNN (Hanocka et al., 2019)","metrics":{"Accuracy":"87.7"},"uses_additional_data":false,"paper_date":"2021-06-04","paper":"/paper/subdivision-based-mesh-convolution-networks","paper_url":"https://arxiv.org/abs/2106.02285v2","paper_title":"Subdivision-Based Mesh Convolution Networks","code":"https://github.com/lzhengning/SubdivNet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Pointnet++ (Qi et al., [2017b])","metrics":{"Accuracy":"82.3"},"uses_additional_data":false,"paper_date":"2021-06-04","paper":"/paper/subdivision-based-mesh-convolution-networks","paper_url":"https://arxiv.org/abs/2106.02285v2","paper_title":"Subdivision-Based Mesh Convolution Networks","code":"https://github.com/lzhengning/SubdivNet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"Pointnet (Qi et al., [2017a])","metrics":{"Accuracy":"74.7"},"uses_additional_data":false,"paper_date":"2021-06-04","paper":"/paper/subdivision-based-mesh-convolution-networks","paper_url":"https://arxiv.org/abs/2106.02285v2","paper_title":"Subdivision-Based Mesh Convolution Networks","code":"https://github.com/lzhengning/SubdivNet","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":4,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":5,"n_samples":5,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":20,"n_samples":20,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}