{"id":"https://openalex.org/W7139010053","doi":"https://doi.org/10.48550/arxiv.2603.16943","title":"KGS-GCN: Enhancing Sparse Skeleton Sensing via Kinematics-Driven Gaussian Splatting and Probabilistic Topology for Action Recognition","display_name":"KGS-GCN: Enhancing Sparse Skeleton Sensing via Kinematics-Driven Gaussian Splatting and Probabilistic Topology for Action Recognition","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7139010053","doi":"https://doi.org/10.48550/arxiv.2603.16943"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.16943","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16943","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.16943","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129935206","display_name":"Yuhan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Yuhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101474225","display_name":"Y.Q. Shi","orcid":"https://orcid.org/0009-0001-6813-0309"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Yicui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121245273","display_name":"Guofa Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Guofa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130054892","display_name":"Liping Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Liping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130163456","display_name":"Jie Li (15030)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129898691","display_name":"Jiaxin Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jiaxin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129989347","display_name":"Wenbo Chu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Wenbo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5129935206"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.8561000227928162,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.8561000227928162,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.03229999914765358,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.0203000009059906,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.49140000343322754},{"id":"https://openalex.org/keywords/restricted-isometry-property","display_name":"Restricted isometry property","score":0.4848000109195709},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4657999873161316},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.4544000029563904},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.4449000060558319},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4413999915122986},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.42640000581741333},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.3758000135421753},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3481000065803528},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.34360000491142273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7598999738693237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4997999966144562},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C17902559","wikidata":"https://www.wikidata.org/wiki/Q17099734","display_name":"Restricted isometry property","level":3,"score":0.4848000109195709},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4657999873161316},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.4544000029563904},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4413999915122986},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.42640000581741333},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35409998893737793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3278000056743622},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.31130000948905945},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3109000027179718},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2992999851703644},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28119999170303345},{"id":"https://openalex.org/C181095308","wikidata":"https://www.wikidata.org/wiki/Q1541599","display_name":"Geometric primitive","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C2874115","wikidata":"https://www.wikidata.org/wiki/Q17099562","display_name":"Persistent homology","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C2776477805","wikidata":"https://www.wikidata.org/wiki/Q4460773","display_name":"Topological data analysis","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.25920000672340393},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.16943","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16943","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.16943","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16943","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Skeleton-based":[0],"action":[1],"recognition":[2],"is":[3,99,144,170],"widely":[4],"utilized":[5],"in":[6,127,226],"sensor":[7,17,44,79,214],"systems":[8],"including":[9],"human-computer":[10],"interaction":[11],"and":[12,82],"intelligent":[13],"surveillance.":[14],"Nevertheless,":[15],"current":[16],"devices":[18],"typically":[19],"generate":[20],"sparse":[21,119,203],"skeleton":[22,120],"data":[23,80],"as":[24],"discrete":[25,87],"coordinates,":[26],"which":[27],"inevitably":[28],"discards":[29],"fine-grained":[30],"spatiotemporal":[31,128,195],"details":[32],"during":[33],"highly":[34],"dynamic":[35],"movements.":[36],"Moreover,":[37],"the":[38,47,76,133,159,167,174,191,199],"rigid":[39],"constraints":[40],"of":[41,49,78,135,193,202],"predefined":[42],"physical":[43,137],"topologies":[45],"hinder":[46],"modeling":[48,192],"latent":[50],"long-range":[51],"dependencies.":[52],"To":[53],"overcome":[54],"these":[55],"limitations,":[56],"we":[57],"propose":[58],"KGS-GCN,":[59],"a":[60,94,139,179,208,219],"graph":[61],"convolutional":[62],"network":[63],"that":[64,187],"integrates":[65],"kinematics-driven":[66,95],"Gaussian":[67,96,164],"splatting":[68,97],"with":[69],"probabilistic":[70,140],"topology.":[71],"Our":[72],"framework":[73,206],"explicitly":[74],"addresses":[75],"challenges":[77],"sparsity":[81],"topological":[83],"rigidity":[84],"by":[85,117,154,173],"transforming":[86],"joints":[88],"into":[89,122],"continuous":[90,124],"generative":[91],"representations.":[92],"Firstly,":[93],"module":[98,113],"designed":[100],"to":[101,131],"dynamically":[102],"construct":[103],"anisotropic":[104],"covariance":[105],"matrices":[106],"using":[107],"instantaneous":[108],"joint":[109,163],"velocity":[110],"vectors.":[111],"This":[112,146,216],"enhances":[114,190],"visual":[115,176,180],"representation":[116],"rendering":[118],"sequences":[121],"multi-view":[123],"heatmaps":[125],"rich":[126],"semantics.":[129],"Secondly,":[130],"transcend":[132],"limitations":[134,201],"fixed":[136],"connections,":[138],"topology":[141],"construction":[142],"method":[143],"proposed.":[145],"approach":[147,217],"generates":[148],"an":[149],"adaptive":[150],"prior":[151],"adjacency":[152],"matrix":[153],"quantifying":[155],"statistical":[156],"correlations":[157],"via":[158,178],"Bhattacharyya":[160],"distance":[161],"between":[162],"distributions.":[165],"Ultimately,":[166],"GCN":[168],"backbone":[169],"adaptively":[171],"modulated":[172],"rendered":[175],"features":[177],"context":[181],"gating":[182],"mechanism.":[183],"Empirical":[184],"results":[185],"demonstrate":[186],"KGS-GCN":[188],"significantly":[189],"complex":[194],"dynamics.":[196],"By":[197],"addressing":[198],"inherent":[200],"inputs,":[204],"our":[205],"offers":[207],"robust":[209],"solution":[210],"for":[211,222],"processing":[212],"low-fidelity":[213],"data.":[215],"establishes":[218],"practical":[220],"pathway":[221],"improving":[223],"perceptual":[224],"reliability":[225],"real-world":[227],"sensing":[228],"applications.":[229]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}