{"url":"/task/rf-based-pose-estimation","name":"RF-based Pose Estimation","slug":"rf-based-pose-estimation","description_markdown":"Detect human actions through walls and occlusions, and in poor lighting conditions. Taking radio frequency (RF) signals as input (e.g. Wifi), generating 3D human skeletons as an intermediate representation, and recognizing actions and interactions.\r\n\r\nSee e.g. RF-Pose from MIT for a good illustration of the approach\r\nhttp://rfpose.csail.mit.edu/\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Making the Invisible Visible](https://arxiv.org/pdf/1909.09300v1.pdf) )</span>","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":16,"papers_with_code":3,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/rf-based-pose-estimation-on-rf-mmd","slug":"rf-based-pose-estimation-on-rf-mmd","dataset":"RF-MMD","dataset_url":null,"rows_in_archive":2,"metrics":["mAP (@0.1, Through-wall)","mAP (@0.1, Visible)"],"first_row_in_archive_order":{"model":"HCN","paper_title":"Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation","paper_url":"/paper/co-occurrence-feature-learning-from-skeleton","paper_date":"2018-04-17","arxiv_id":"1804.06055","code_links":[{"title":"huguyuehuhu/HCN-pytorch","url":"https://github.com/huguyuehuhu/HCN-pytorch"},{"title":"hikvision-research/skelact","url":"https://github.com/hikvision-research/skelact"},{"title":"fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition","url":"https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition"},{"title":"maxstrobel/HCN-PrototypeLoss-PyTorch","url":"https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch"},{"title":"hhe-distance/AIF-CNN","url":"https://github.com/hhe-distance/AIF-CNN"},{"title":"natepuppy/HCN-pytorch","url":"https://github.com/natepuppy/HCN-pytorch"}],"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":3}}},{"leaderboard":"/sota/rf-based-pose-estimation-on-rf-mmd-1","slug":"rf-based-pose-estimation-on-rf-mmd-1","dataset":"RF-MMD","dataset_url":null,"rows_in_archive":1,"metrics":["mAP (@0.1, Through-wall)","mAP (@0.1, Visible)"],"first_row_in_archive_order":{"model":"RF-Action","paper_title":"Making the Invisible Visible: Action Recognition Through Walls and Occlusions","paper_url":"/paper/making-the-invisible-visible-action","paper_date":"2019-09-20","arxiv_id":"1909.09300","code_links":[],"syntology":null}}],"datasets":[],"subtasks":[],"parent_tasks":[{"url":"/task/pose-estimation","name":"Pose Estimation"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":3,"of":3,"tagged_in_all":16,"items":[{"url":"/paper/co-occurrence-feature-learning-from-skeleton","title":"Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation","date":"2018-04-17","arxiv_id":"1804.06055","repositories_listed":6,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":3}},{"url":"/paper/person-in-wifi-fine-grained-person-perception","title":"Person-in-WiFi: Fine-grained Person Perception using WiFi","date":"2019-03-30","arxiv_id":"1904.00276","repositories_listed":1,"syntology":null},{"url":"/paper/can-wifi-estimate-person-pose","title":"Can WiFi Estimate Person Pose?","date":"2019-03-30","arxiv_id":"1904.00277","repositories_listed":1,"syntology":null}],"syntology_records":1,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","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)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}