{"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":"/paper/human-pose-regression-by-combining-indirect","title":"Human Pose Regression by Combining Indirect Part Detection and Contextual Information","arxiv_id":"1710.02322","date":"2017-10-06","proceeding":null,"authors":["Diogo C. Luvizon","Hedi Tabia","David Picard"],"abstract":"In this paper, we propose an end-to-end trainable regression approach for\nhuman pose estimation from still images. We use the proposed Soft-argmax\nfunction to convert feature maps directly to joint coordinates, resulting in a\nfully differentiable framework. Our method is able to learn heat maps\nrepresentations indirectly, without additional steps of artificial ground truth\ngeneration. Consequently, contextual information can be included to the pose\npredictions in a seamless way. We evaluated our method on two very challenging\ndatasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets,\nreaching the best performance among all the existing regression methods and\ncomparable results to the state-of-the-art detection based approaches.","url_abs":"http://arxiv.org/abs/1710.02322v1","url_pdf":"http://arxiv.org/pdf/1710.02322v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"human-pose-regression-by-combining-indirect","repo_url":"https://github.com/dluvizon/pose-regression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pose-estimation-on-leeds-sports-poses","task":"Pose Estimation","dataset":"Leeds Sports Poses","model":"Soft-argmax + contextual information","rank_in_archive_order":12,"of":18,"metrics":{"PCK":"90.5%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.02322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.02322"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dluvizon/pose-regression","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"dccf225a0279fdfa","entry":"sepconv_residual","repo":"dluvizon/pose-regression","repo_kind":"official","path":"posereg/network.py","file_url":"https://github.com/dluvizon/pose-regression/blob/HEAD/posereg/network.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dccf225a0279fdfa"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}