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However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be difficult to obtain. In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences. To this end, we design our network to model a proposal distribution which we use to approximate the unknown multi-modal target posterior distribution. We achieve the approximation by minimizing the KL divergence between the proposal and target distributions, and this leads to a 2D reprojection error and a prior loss term that can be weakly supervised. Furthermore, we determine the most probable solution as the conditional mode of the samples using the mean-shift algorithm. We evaluate our method on three benchmark datasets -- Human3.6M, MPII and MPI-INF-3DHP. Experimental results show that our approach is capable of generating multiple feasible hypotheses and achieves state-of-the-art results compared to existing weakly supervised approaches. Our source code is available at the project website.","url_abs":"https://arxiv.org/abs/2008.05770v1","url_pdf":"https://arxiv.org/pdf/2008.05770v1.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":"weakly-supervised-generative-network-for","repo_url":"https://github.com/chaneyddtt/weakly-supervised-3d-pose-generator","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"multi-hypotheses-3d-human-pose-estimation","task_name":"Multi-Hypotheses 3D Human Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-mpi-inf-3dhp","task":"3D Human Pose Estimation","dataset":"MPI-INF-3DHP","model":"WSGAN","rank_in_archive_order":106,"of":108,"metrics":{"PCK":"79.3"},"uses_additional_data":false},{"leaderboard":"/sota/multi-hypotheses-3d-human-pose-estimation-on","task":"Multi-Hypotheses 3D Human Pose Estimation","dataset":"Human3.6M","model":"Li et al.","rank_in_archive_order":11,"of":12,"metrics":{"Average MPJPE (mm)":"73.9","Average PMPJPE (mm)":"44.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2008.05770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05770"}},"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. 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