{"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/neuralannot-neural-annotator-for-in-the-wild","title":"NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets","arxiv_id":"2011.11232","date":"2020-11-23","proceeding":null,"authors":["Gyeongsik Moon","Hongsuk Choi","Kyoung Mu Lee"],"abstract":"Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the pre-processing stage of the regressors. The fitted 3D parameters at the last fitting iteration become the 3D pseudo-GTs, used to fully supervise the regressors. Optimization-based annotators, such as SMPLify-X, have been widely used to obtain the 3D pseudo-GTs. However, they often produce wrong 3D pseudo-GTs as they fit the 3D parameters to GT of each sample independently. To overcome the limitation, we present NeuralAnnot, a neural network-based annotator. The main idea of NeuralAnnot is to employ a neural network-based regressor and dedicate it for the annotation. Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly supervised with GT 2D/3D joint coordinates of training sets. The testing results on the same training sets become 3D pseudo-GTs, used to fully supervise the regressors. We show that 3D pseudo-GTs of NeuralAnnot are highly beneficial to train the regressors. We made our 3D pseudo-GTs publicly available.","url_abs":"https://arxiv.org/abs/2011.11232v5","url_pdf":"https://arxiv.org/pdf/2011.11232v5.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":"neuralannot-neural-annotator-for-in-the-wild","repo_url":"https://github.com/mks0601/NeuralAnnot_RELEASE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neuralannot-neural-annotator-for-in-the-wild","repo_url":"https://github.com/facebookresearch/InterHand2.6M","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neuralannot-neural-annotator-for-in-the-wild","repo_url":"https://github.com/hongsukchoi/Pose2Mesh_RELEASE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neuralannot-neural-annotator-for-in-the-wild","repo_url":"https://github.com/hongsukchoi/TCMR_RELEASE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neuralannot-neural-annotator-for-in-the-wild","repo_url":"https://github.com/jaehakim97/blurhand_release","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2011.11232","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.11232"}},"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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