{"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/neural-body-fitting-unifying-deep-learning","title":"Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation","arxiv_id":"1808.05942","date":"2018-08-17","proceeding":null,"authors":["Mohamed Omran","Christoph Lassner","Gerard Pons-Moll","Peter V. Gehler","Bernt Schiele"],"abstract":"Direct prediction of 3D body pose and shape remains a challenge even for\nhighly parameterized deep learning models. Mapping from the 2D image space to\nthe prediction space is difficult: perspective ambiguities make the loss\nfunction noisy and training data is scarce. In this paper, we propose a novel\napproach (Neural Body Fitting (NBF)). It integrates a statistical body model\nwithin a CNN, leveraging reliable bottom-up semantic body part segmentation and\nrobust top-down body model constraints. NBF is fully differentiable and can be\ntrained using 2D and 3D annotations. In detailed experiments, we analyze how\nthe components of our model affect performance, especially the use of part\nsegmentations as an explicit intermediate representation, and present a robust,\nefficiently trainable framework for 3D human pose estimation from 2D images\nwith competitive results on standard benchmarks. Code will be made available at\nhttp://github.com/mohomran/neural_body_fitting","url_abs":"http://arxiv.org/abs/1808.05942v1","url_pdf":"http://arxiv.org/pdf/1808.05942v1.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":"neural-body-fitting-unifying-deep-learning","repo_url":"https://github.com/mohomran/neural_body_fitting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"neural-body-fitting-unifying-deep-learning","repo_url":"https://github.com/andrewjong/SwapNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"monocular-3d-human-pose-estimation","task_name":"Monocular 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-humaneva-i","task":"3D Human Pose Estimation","dataset":"HumanEva-I","model":"Ours","rank_in_archive_order":28,"of":31,"metrics":{"Mean Reconstruction Error (mm)":"64"},"uses_additional_data":false},{"leaderboard":"/sota/monocular-3d-human-pose-estimation-on-human3","task":"Monocular 3D Human Pose Estimation","dataset":"Human3.6M","model":"Neural Body Fitting\n(NBF)","rank_in_archive_order":42,"of":52,"metrics":{"Frames Needed":"1","Need Ground Truth 2D Pose":"No","Use Video Sequence":"No"},"uses_additional_data":false},{"leaderboard":"/sota/monocular-3d-human-pose-estimation-on-human3","task":"Monocular 3D Human Pose Estimation","dataset":"Human3.6M","model":"Neural Body Fitting (NBF)","rank_in_archive_order":51,"of":52,"metrics":{"PA-MPJPE":"59.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.05942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.05942"}},"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/mohomran/neural_body_fitting","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/andrewjong/SwapNet","reach":{"status":"unanswered"}}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"89071b4f66a64eeb","entry":"adjust_config","repo":"mohomran/neural_body_fitting","repo_kind":"official","path":"experiments/config/demo_up/config.py","file_url":"https://github.com/mohomran/neural_body_fitting/blob/HEAD/experiments/config/demo_up/config.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"89071b4f66a64eeb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}