{"id":"https://openalex.org/W4313038941","doi":"https://doi.org/10.1109/icpr56361.2022.9956131","title":"OPEN: Order-preserving Pointcloud Encoder Decoder Network for Body Shape Refinement","display_name":"OPEN: Order-preserving Pointcloud Encoder Decoder Network for Body Shape Refinement","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4313038941","doi":"https://doi.org/10.1109/icpr56361.2022.9956131"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956131","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5047388111","display_name":"Haidong Zhu","orcid":"https://orcid.org/0000-0002-4763-8998"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haidong Zhu","raw_affiliation_strings":["University of Southern Califormia,Department of Computer Science,Los Angeles,California,90089"],"affiliations":[{"raw_affiliation_string":"University of Southern Califormia,Department of Computer Science,Los Angeles,California,90089","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081199756","display_name":"Ye Yuan","orcid":"https://orcid.org/0000-0001-5316-6002"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["Bytedance Inc.,Mountain View,California,94041"],"affiliations":[{"raw_affiliation_string":"Bytedance Inc.,Mountain View,California,94041","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052610760","display_name":"Yiheng Zhu","orcid":"https://orcid.org/0000-0002-3857-1533"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiheng Zhu","raw_affiliation_strings":["Bytedance Inc.,Mountain View,California,94041"],"affiliations":[{"raw_affiliation_string":"Bytedance Inc.,Mountain View,California,94041","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101679371","display_name":"Xiao Yang","orcid":"https://orcid.org/0000-0001-6096-9903"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Yang","raw_affiliation_strings":["Bytedance Inc.,Mountain View,California,94041"],"affiliations":[{"raw_affiliation_string":"Bytedance Inc.,Mountain View,California,94041","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080385311","display_name":"Ram Nevatia","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ram Nevatia","raw_affiliation_strings":["University of Southern Califormia,Department of Computer Science,Los Angeles,California,90089"],"affiliations":[{"raw_affiliation_string":"University of Southern Califormia,Department of Computer Science,Los Angeles,California,90089","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5047388111"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.8253,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.82136602,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"29","issue":null,"first_page":"521","last_page":"527"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9926000237464905,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7560546398162842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.749575138092041},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6980766654014587},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6709393262863159},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6614077091217041},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6152849197387695},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6114252209663391},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5252246856689453},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5077039003372192},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4588170647621155},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4311135411262512},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32847535610198975}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7560546398162842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.749575138092041},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6980766654014587},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6709393262863159},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6614077091217041},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6152849197387695},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6114252209663391},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5252246856689453},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5077039003372192},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4588170647621155},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4311135411262512},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32847535610198975},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956131","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1956271161","https://openalex.org/W1967554269","https://openalex.org/W2174133987","https://openalex.org/W2194775991","https://openalex.org/W2469266052","https://openalex.org/W2483862638","https://openalex.org/W2511691466","https://openalex.org/W2546066744","https://openalex.org/W2560722161","https://openalex.org/W2582734987","https://openalex.org/W2768683308","https://openalex.org/W2796426482","https://openalex.org/W2798314605","https://openalex.org/W2798670728","https://openalex.org/W2883221003","https://openalex.org/W2949671016","https://openalex.org/W2962778872","https://openalex.org/W2962849139","https://openalex.org/W2963627347","https://openalex.org/W2963850211","https://openalex.org/W2963926543","https://openalex.org/W2981637078","https://openalex.org/W2981978060","https://openalex.org/W3012056513","https://openalex.org/W3014641072","https://openalex.org/W3035291735","https://openalex.org/W3053462477","https://openalex.org/W3095682719","https://openalex.org/W3107471220","https://openalex.org/W3117476483","https://openalex.org/W4233857083","https://openalex.org/W4297692481","https://openalex.org/W6725627366","https://openalex.org/W6729001083","https://openalex.org/W6748208425","https://openalex.org/W6750179221","https://openalex.org/W6753219919","https://openalex.org/W6757867559","https://openalex.org/W6767071366"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W4294770367"],"abstract_inverted_index":{"Image-based":[0],"3-D":[1,25,110],"human":[2,26,82],"body":[3,27,83],"shape":[4,46,84],"estimation":[5],"and":[6,37,55,123],"reconstruction":[7],"have":[8],"shown":[9],"significant":[10],"improvement":[11],"by":[12],"using":[13,102],"deep":[14],"neural":[15],"networks.":[16],"Compared":[17],"with":[18,87,128],"reconstructing":[19,24],"from":[20,29,85],"a":[21,70],"single":[22],"image,":[23],"shapes":[28,62],"video":[30],"or":[31],"image":[32],"sequences":[33],"requires":[34],"high":[35,53],"precision":[36],"dense":[38,58,97],"correspondences":[39],"between":[40,60],"the":[41,44,57,80,88,120,129],"keypoints":[42],"of":[43,90],"reconstructed":[45,81],"sequence.":[47],"Existing":[48],"methods":[49,118],"cannot":[50],"achieve":[51],"both":[52],"accuracy":[54],"keep":[56],"correspondence":[59],"different":[61],"after":[63],"reconstruction.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"propose":[69],"method":[71],"named":[72],"Order-preserving":[73],"Point":[74],"cloud":[75],"Encoder-decoder":[76],"Network":[77],"to":[78],"refine":[79],"SMPL":[86],"assistance":[89],"RGB":[91,104],"images":[92,105],"while":[93],"preserving":[94],"its":[95],"original":[96],"correspondence.":[98],"We":[99,115],"further":[100],"introduce":[101],"2-D":[103],"as":[106],"weak":[107],"supervision":[108],"when":[109],"labels":[111],"are":[112],"not":[113],"available.":[114],"assess":[116],"our":[117],"on":[119],"public":[121],"dataset":[122],"show":[124],"improved":[125],"results":[126],"compared":[127],"baseline":[130],"methods.":[131]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}