{"id":"https://openalex.org/W4413147403","doi":"https://doi.org/10.1109/cvpr52734.2025.00521","title":"GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion","display_name":"GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion","publication_year":2025,"publication_date":"2025-06-10","ids":{"openalex":"https://openalex.org/W4413147403","doi":"https://doi.org/10.1109/cvpr52734.2025.00521"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52734.2025.00521","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.00521","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5015737161","display_name":"Jiapeng Tang","orcid":"https://orcid.org/0009-0000-2939-0055"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jiapeng Tang","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093427267","display_name":"Davide Davoli","orcid":null},"institutions":[{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Davide Davoli","raw_affiliation_strings":["Toyota Motor Europe NV/SA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Motor Europe NV/SA","institution_ids":["https://openalex.org/I4210093665"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048779944","display_name":"Tobias Kirschstein","orcid":"https://orcid.org/0009-0002-5308-591X"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tobias Kirschstein","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025798902","display_name":"Liam Schoneveld","orcid":"https://orcid.org/0000-0002-7324-6234"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liam Schoneveld","raw_affiliation_strings":["Woven by Toyota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Woven by Toyota","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088583491","display_name":"Matthias Nie\u00dfner","orcid":"https://orcid.org/0000-0001-6093-5199"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias Nie\u00dfner","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.4796,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.9744296,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5546","last_page":"5558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9839000105857849,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9839000105857849,"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":0.9451000094413757,"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"}},{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.944100022315979,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/avatar","display_name":"Avatar","score":0.8570585250854492},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.7476650476455688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6907094717025757},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6076210737228394},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5603215098381042},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5275558829307556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5167459845542908},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.47677165269851685},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.29967570304870605},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09090486168861389}],"concepts":[{"id":"https://openalex.org/C2777365542","wikidata":"https://www.wikidata.org/wiki/Q83090","display_name":"Avatar","level":2,"score":0.8570585250854492},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.7476650476455688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6907094717025757},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6076210737228394},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5603215098381042},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5275558829307556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5167459845542908},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.47677165269851685},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.29967570304870605},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09090486168861389},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52734.2025.00521","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.00521","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311976","display_name":"Scan|Design Foundation by Inger and Jens Bruun","ror":"https://ror.org/01rsgv203"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3138471234","https://openalex.org/W4247958311","https://openalex.org/W4396832849","https://openalex.org/W1584662471","https://openalex.org/W2785089443","https://openalex.org/W2265117524","https://openalex.org/W1467576422","https://openalex.org/W4220730560","https://openalex.org/W2969390373","https://openalex.org/W2604471456"],"abstract_inverted_index":{"We":[0,93,155],"propose":[1],"a":[2,53],"novel":[3,45,171],"approach":[4],"for":[5],"reconstructing":[6],"animatable":[7],"3D":[8,21],"Gaussian":[9,71,115],"avatars":[10],"from":[11,25,84,103,180],"monocular":[12,181],"videos":[13,182],"captured":[14,183],"by":[15,123],"commodity":[16,185],"devices":[17],"like":[18],"smartphones.":[19],"Photorealistic":[20],"head":[22,55,86],"avatar":[23,116,178],"reconstruction":[24],"such":[26],"recordings":[27],"is":[28],"challenging":[29],"due":[30],"to":[31,42,61,107,144],"limited":[32],"observations,":[33],"which":[34,88],"leaves":[35],"unobserved":[36],"regions":[37,65],"under-constrained":[38],"and":[39,66,111],"can":[40],"lead":[41],"artifacts":[43],"in":[44,63,70,170],"views.":[46],"To":[47,74,135],"address":[48],"this":[49],"problem,":[50],"we":[51,79,118,139,175],"introduce":[52],"multi-view":[54,120],"diffusion":[56,97,121],"model,":[57],"leveraging":[58],"its":[59],"priors":[60,122,143],"fill":[62],"missing":[64],"ensure":[67],"view":[68,172],"consistency":[69],"splatting":[72],"renderings.":[73],"enable":[75],"precise":[76],"viewpoint":[77],"control,":[78],"use":[80],"normal":[81],"maps":[82],"rendered":[83],"FLAME-based":[85],"reconstruction,":[87,117],"provides":[89],"pixel-aligned":[90],"inductive":[91],"biases.":[92],"also":[94],"condition":[95],"the":[96,104,146,160],"model":[98],"on":[99,159,184],"VAE":[100],"features":[101],"extracted":[102],"input":[105],"image":[106],"preserve":[108],"facial":[109],"identity":[110],"appearance":[112],"details.":[113],"For":[114],"distill":[119],"using":[124],"iteratively":[125],"denoised":[126,147],"images":[127],"as":[128],"pseudo-ground":[129],"truths,":[130],"effectively":[131],"mitigating":[132],"over-saturation":[133],"issues.":[134],"further":[136],"improve":[137],"photorealism,":[138],"apply":[140],"latent":[141,148],"upsampling":[142],"refine":[145],"before":[149],"decoding":[150],"it":[151],"into":[152],"an":[153],"image.":[154],"evaluate":[156],"our":[157],"method":[158],"NeRSemble":[161],"dataset,":[162],"showing":[163],"that":[164],"GAF":[165],"outperforms":[166],"previous":[167],"state-of-the-art":[168],"methods":[169],"synthesis.":[173],"Furthermore,":[174],"demonstrate":[176],"higher-fidelity":[177],"reconstructions":[179],"devices.":[186],"Project":[187],"Page:":[188],"https://tangjiapeng.github.io/projects/GAF":[189]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}