{"id":"https://openalex.org/W7162511455","doi":"https://doi.org/10.1109/3dv69130.2026.00088","title":"Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture","display_name":"Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7162511455","doi":"https://doi.org/10.1109/3dv69130.2026.00088"},"language":null,"primary_location":{"id":"doi:10.1109/3dv69130.2026.00088","is_oa":false,"landing_page_url":"https://doi.org/10.1109/3dv69130.2026.00088","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on 3D Vision (3DV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062321523","display_name":"Tom Wehrbein","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tom Wehrbein","raw_affiliation_strings":["L3S - Leibniz University Hannover,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S - Leibniz University Hannover,Germany","institution_ids":["https://openalex.org/I4210136150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040412734","display_name":"Bodo Rosenhahn","orcid":"https://orcid.org/0000-0003-3861-1424"},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bodo Rosenhahn","raw_affiliation_strings":["L3S - Leibniz University Hannover,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L3S - Leibniz University Hannover,Germany","institution_ids":["https://openalex.org/I4210136150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85274239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"871","last_page":"881"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.6561999917030334,"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":0.6561999917030334,"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":0.2029999941587448,"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":0.043800000101327896,"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/motion-capture","display_name":"Motion capture","score":0.7480000257492065},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4948999881744385},{"id":"https://openalex.org/keywords/human-motion","display_name":"Human motion","score":0.4828999936580658},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.446399986743927},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.36899998784065247},{"id":"https://openalex.org/keywords/visual-hull","display_name":"Visual hull","score":0.3418000042438507}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7738000154495239},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.7480000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7087000012397766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.571399986743927},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4948999881744385},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.4828999936580658},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.446399986743927},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C2776863239","wikidata":"https://www.wikidata.org/wiki/Q7936601","display_name":"Visual hull","level":3,"score":0.3418000042438507},{"id":"https://openalex.org/C115076146","wikidata":"https://www.wikidata.org/wiki/Q1651051","display_name":"Foot (prosody)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C146159030","wikidata":"https://www.wikidata.org/wiki/Q7625099","display_name":"Structure from motion","level":3,"score":0.3249000012874603},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C95020103","wikidata":"https://www.wikidata.org/wiki/Q1813492","display_name":"Match moving","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C104582849","wikidata":"https://www.wikidata.org/wiki/Q787282","display_name":"Automatic identification and data capture","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/3dv69130.2026.00088","is_oa":false,"landing_page_url":"https://doi.org/10.1109/3dv69130.2026.00088","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on 3D Vision (3DV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4215611517429352,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1967554269","https://openalex.org/W1992475172","https://openalex.org/W2080873731","https://openalex.org/W2101032778","https://openalex.org/W2483862638","https://openalex.org/W2545173102","https://openalex.org/W2576289912","https://openalex.org/W2797184202","https://openalex.org/W2895748257","https://openalex.org/W2916798096","https://openalex.org/W2949924544","https://openalex.org/W2962730651","https://openalex.org/W2963515833","https://openalex.org/W2963995996","https://openalex.org/W2964179555","https://openalex.org/W2971856312","https://openalex.org/W2975420824","https://openalex.org/W2978956737","https://openalex.org/W2981637078","https://openalex.org/W3010245114","https://openalex.org/W3035551320","https://openalex.org/W3035581100","https://openalex.org/W3044670941","https://openalex.org/W3106857494","https://openalex.org/W3110017807","https://openalex.org/W3128810022","https://openalex.org/W3174980830","https://openalex.org/W3180730526","https://openalex.org/W3196703792","https://openalex.org/W3204956438","https://openalex.org/W4205116876","https://openalex.org/W4214586188","https://openalex.org/W4214679856","https://openalex.org/W4221142859","https://openalex.org/W4225609743","https://openalex.org/W4290715204","https://openalex.org/W4292828485","https://openalex.org/W4312261745","https://openalex.org/W4312518484","https://openalex.org/W4312979714","https://openalex.org/W4376481254","https://openalex.org/W4386071628","https://openalex.org/W4386075709","https://openalex.org/W4386076086","https://openalex.org/W4386076106","https://openalex.org/W4386076574","https://openalex.org/W4386076655","https://openalex.org/W4388979610","https://openalex.org/W4390190334","https://openalex.org/W4390190719","https://openalex.org/W4390873286","https://openalex.org/W4390874109","https://openalex.org/W4390874306","https://openalex.org/W4399563538","https://openalex.org/W4402715826","https://openalex.org/W4402716184","https://openalex.org/W4402951553","https://openalex.org/W4403624672","https://openalex.org/W4403842054","https://openalex.org/W4403890006","https://openalex.org/W4403947180","https://openalex.org/W4409263927","https://openalex.org/W4413147687","https://openalex.org/W4413513664","https://openalex.org/W4413556690"],"related_works":[],"abstract_inverted_index":{"State-of-the-art":[0],"methods":[1],"can":[2,93],"recover":[3],"accurate":[4],"overall":[5],"3D":[6],"human":[7,70],"body":[8],"motion":[9,49,65,97,111,147],"from":[10,39],"in-the-wild":[11],"videos.":[12],"However,":[13],"they":[14],"often":[15],"fail":[16],"to":[17,79,121,182],"capture":[18,98],"fine-grained":[19],"articulations,":[20],"especially":[21],"in":[22,131],"the":[23,186],"feet,":[24],"which":[25],"are":[26,194],"critical":[27],"for":[28,196],"applications":[29],"such":[30],"as":[31,112],"gait":[32],"analysis":[33],"and":[34,46,92,109,114,137,165,192],"animation.":[35],"This":[36],"limitation":[37],"results":[38],"training":[40],"datasets":[41],"with":[42,55],"inaccurate":[43,88],"foot":[44,48,64,76,110,118,123,146,159],"annotations":[45],"limited":[47],"diversity.":[50],"We":[51],"address":[52],"this":[53],"gap":[54],"FootMR,":[56],"a":[57,152],"Foot":[58],"Motion":[59],"Refinement":[60],"method":[61],"that":[62,168],"refines":[63],"estimated":[66],"by":[67,128,180],"an":[68],"existing":[69],"recovery":[71],"model":[72],"through":[73],"lifting":[74],"2D":[75],"keypoint":[77],"sequences":[78],"3D.":[80],"By":[81],"avoiding":[82],"direct":[83],"image":[84],"input,":[85],"FootMR":[86,106,169],"circumvents":[87],"image-3D":[89],"annotation":[90],"pairs":[91],"instead":[94],"leverage":[95],"large-scale":[96],"data.":[99],"To":[100,142],"resolve":[101],"ambiguities":[102],"of":[103,145,157],"2D-to-3D":[104],"lifting,":[105],"incorporates":[107],"knee":[108],"context":[113],"predicts":[115],"only":[116],"residual":[117],"motion.":[119],"Generalization":[120],"extreme":[122],"poses":[124],"is":[125],"further":[126],"improved":[127],"representing":[129],"joints":[130],"global":[132],"rather":[133],"than":[134],"parent-relative":[135],"rotations":[136],"applying":[138],"extensive":[139],"data":[140],"augmentation.":[141],"support":[143],"evaluation":[144],"reconstruction,":[148],"we":[149],"introduce":[150],"MOOF,":[151,163],"<tex":[153],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[154],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$2D$</tex>":[155],"dataset":[156,193],"complex":[158],"movements.":[160],"Experiments":[161],"on":[162,178],"MOYO,":[164],"RICH":[166],"show":[167],"outperforms":[170],"state-of-the-art":[171],"methods,":[172],"reducing":[173],"ankle":[174],"joint":[175],"angle":[176],"error":[177],"MOYO":[179],"up":[181],"30":[183],"%":[184],"over":[185],"best":[187],"video-based":[188],"approach.":[189],"Our":[190],"code":[191],"available":[195],"research":[197],"purposes":[198],"at":[199],"twehrbein.github.io/footmr-website/.":[200]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-28T00:00:00"}
