{"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/skeleton-parted-graph-scattering-networks-for","title":"Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction","arxiv_id":"2208.00368","date":"2022-07-31","proceeding":null,"authors":["Maosen Li","Siheng Chen","Zijing Zhang","Lingxi Xie","Qi Tian","Ya zhang"],"abstract":"Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these methods have two critical issues: first, deep graph convolutions filter features within only limited graph spectrums, losing sufficient information in the full band; second, using a single graph to model the whole body underestimates the diverse patterns on various body-parts. To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands. To address the second issue, body-parts are modeled separately to learn diverse dynamics, which enables finer feature extraction along the spatial dimensions. Integrating the above two designs, we propose a novel skeleton-parted graph scattering network (SPGSN). The cores of the model are cascaded multi-part graph scattering blocks (MPGSBs), building adaptive graph scattering on diverse body-parts, as well as fusing the decomposed features based on the inferred spectrum importance and body-part interactions. Extensive experiments have shown that SPGSN outperforms state-of-the-art methods by remarkable margins of 13.8%, 9.3% and 2.7% in terms of 3D mean per joint position error (MPJPE) on Human3.6M, CMU Mocap and 3DPW datasets, respectively.","url_abs":"https://arxiv.org/abs/2208.00368v1","url_pdf":"https://arxiv.org/pdf/2208.00368v1.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":"skeleton-parted-graph-scattering-networks-for","repo_url":"https://github.com/mediabrain-sjtu/spgsn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"human-motion-prediction","task_name":"Human motion prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2208.00368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.00368"}},"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/mediabrain-sjtu/spgsn","reach":null}],"summary":{"ran":2,"unverified":4},"by_repo_kind":{"official":{"samples":6,"ran":2,"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":"cea38175044bfec1","entry":"GraphConvolution","repo":"mediabrain-sjtu/spgsn","repo_kind":"official","path":"utils/model.py","file_url":"https://github.com/mediabrain-sjtu/spgsn/blob/HEAD/utils/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cea38175044bfec1"}},{"code_sha256_prefix":"1d73a2436824038f","entry":"GraphOperate","repo":"mediabrain-sjtu/spgsn","repo_kind":"official","path":"utils/model.py","file_url":"https://github.com/mediabrain-sjtu/spgsn/blob/HEAD/utils/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1d73a2436824038f"}},{"code_sha256_prefix":"3a3dcd4a64e7301e","entry":"GCN","repo":"mediabrain-sjtu/spgsn","repo_kind":"official","path":"utils/model.py","file_url":"https://github.com/mediabrain-sjtu/spgsn/blob/HEAD/utils/model.py","link_basis":"first_harvest_node","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":"3a3dcd4a64e7301e"}},{"code_sha256_prefix":"17bc18da3a212a2d","entry":"GC_Block","repo":"mediabrain-sjtu/spgsn","repo_kind":"official","path":"utils/model.py","file_url":"https://github.com/mediabrain-sjtu/spgsn/blob/HEAD/utils/model.py","link_basis":"first_harvest_node","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":"17bc18da3a212a2d"}},{"code_sha256_prefix":"84aa5b3cd624ad11","entry":"GraphConvScatter","repo":"mediabrain-sjtu/spgsn","repo_kind":"official","path":"utils/model.py","file_url":"https://github.com/mediabrain-sjtu/spgsn/blob/HEAD/utils/model.py","link_basis":"first_harvest_node","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":"84aa5b3cd624ad11"}},{"code_sha256_prefix":"f3e6d13d6677e6fa","entry":"GraphConvScatter_Forest","repo":"mediabrain-sjtu/spgsn","repo_kind":"official","path":"utils/model.py","file_url":"https://github.com/mediabrain-sjtu/spgsn/blob/HEAD/utils/model.py","link_basis":"first_harvest_node","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":"f3e6d13d6677e6fa"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}