{"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/non-local-latent-relation-distillation-for-1","title":"Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation","arxiv_id":"2204.01971","date":"2022-04-05","proceeding":"NeurIPS 2021 12","authors":["Jogendra Nath Kundu","Siddharth Seth","Anirudh Jamkhandi","Pradyumna YM","Varun Jampani","Anirban Chakraborty","R. Venkatesh Babu"],"abstract":"Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient. To this end, we cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target. We propose to infer image-to-pose via two explicit mappings viz. image-to-latent and latent-to-pose where the latter is a pre-learned decoder obtained from a prior-enforcing generative adversarial auto-encoder. Next, we introduce relation distillation as a means to align the unpaired cross-modal samples i.e. the unpaired target videos and unpaired 3D pose sequences. To this end, we propose a new set of non-local relations in order to characterize long-range latent pose interactions unlike general contrastive relations where positive couplings are limited to a local neighborhood structure. Further, we provide an objective way to quantify non-localness in order to select the most effective relation set. We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks.","url_abs":"https://arxiv.org/abs/2204.01971v2","url_pdf":"https://arxiv.org/pdf/2204.01971v2.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":[],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"unsupervised-3d-human-pose-estimation","task_name":"Unsupervised 3D Human Pose Estimation"},{"task_slug":"weakly-supervised-3d-human-pose-estimation","task_name":"Weakly-supervised 3D Human Pose Estimation"}],"methods":[{"method_slug":"align","method_name":"ALIGN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-3dpw","task":"3D Human Pose Estimation","dataset":"3DPW","model":"Non-Local Latent Relation Distillation","rank_in_archive_order":105,"of":119,"metrics":{"PA-MPJPE":"72.1"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-3d-human-pose-estimation-on","task":"Unsupervised 3D Human Pose Estimation","dataset":"Human3.6M","model":"Non-Local Latent Relation Distillation","rank_in_archive_order":6,"of":12,"metrics":{"MPJPE":"97.8","PA-MPJPE":"86.2"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-3d-human-pose-estimation-on","task":"Weakly-supervised 3D Human Pose Estimation","dataset":"Human3.6M","model":"Non-Local Latent Relation Distillation","rank_in_archive_order":8,"of":33,"metrics":{"Average MPJPE (mm)":"57.6","PA-MPJPE":"48.2"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.01971","atlas_url":"https://app.syntology.ai/?focus=2204.01971","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}