{"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/hood-hierarchical-graphs-for-generalized","title":"HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics","arxiv_id":"2212.07242","date":"2022-12-14","proceeding":"CVPR 2023 1","authors":["Artur Grigorev","Bernhard Thomaszewski","Michael J. Black","Otmar Hilliges"],"abstract":"We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Our method furthermore handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that our method outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2212.07242v3","url_pdf":"https://arxiv.org/pdf/2212.07242v3.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":"hood-hierarchical-graphs-for-generalized","repo_url":"https://github.com/Dolorousrtur/HOOD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"hood-hierarchical-graphs-for-generalized","repo_url":"https://github.com/yangyucheng000/Paper-4/tree/main/HOOD-ms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"physical-simulations","task_name":"Physical Simulations"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/physical-simulations-on-4d-dress","task":"Physical Simulations","dataset":"4D-DRESS","model":"HOOD_Lower","rank_in_archive_order":3,"of":12,"metrics":{"Chamfer (cm)":"2.070","Stretching Energy":"0.008"},"uses_additional_data":false},{"leaderboard":"/sota/physical-simulations-on-4d-dress","task":"Physical Simulations","dataset":"4D-DRESS","model":"HOOD_Upper","rank_in_archive_order":5,"of":12,"metrics":{"Chamfer (cm)":"2.668","Stretching Energy":"0.013"},"uses_additional_data":false},{"leaderboard":"/sota/physical-simulations-on-4d-dress","task":"Physical Simulations","dataset":"4D-DRESS","model":"HOOD_Dress","rank_in_archive_order":7,"of":12,"metrics":{"Chamfer (cm)":"4.292","Stretching Energy":"0.010"},"uses_additional_data":false},{"leaderboard":"/sota/physical-simulations-on-4d-dress","task":"Physical Simulations","dataset":"4D-DRESS","model":"HOOD_Outer","rank_in_archive_order":12,"of":12,"metrics":{"Chamfer (cm)":"5.355","Stretching Energy":"0.011"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2212.07242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07242"}},"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/yangyucheng000/Paper-4/tree/main/HOOD-ms","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Dolorousrtur/HOOD","reach":{"status":"ok","spdx":"MIT"}}],"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":"4287dadd22b8144a","entry":"make_edgesets_dict","repo":"Dolorousrtur/HOOD","repo_kind":"official","path":"models/core/base.py","file_url":"https://github.com/Dolorousrtur/HOOD/blob/HEAD/models/core/base.py","link_basis":"harvester_set","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":"4287dadd22b8144a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}