{"id":"https://openalex.org/W7133337934","doi":"https://doi.org/10.48550/arxiv.2603.01163","title":"BeautyGRPO: Aesthetic Alignment for Face Retouching via Dynamic Path Guidance and Fine-Grained Preference Modeling","display_name":"BeautyGRPO: Aesthetic Alignment for Face Retouching via Dynamic Path Guidance and Fine-Grained Preference Modeling","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133337934","doi":"https://doi.org/10.48550/arxiv.2603.01163"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01163","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01163","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.2603.01163","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127890229","display_name":"Jiachen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jiachen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084229156","display_name":"Xianhui Lin","orcid":"https://orcid.org/0000-0002-8974-2064"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Xianhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127964944","display_name":"Yi Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127873222","display_name":"Zebiao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Zebiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127990900","display_name":"Xing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127976844","display_name":"Hong Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062946632","display_name":"Yanmei Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yanmei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.4196999967098236,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.4196999967098236,"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/T11448","display_name":"Face recognition and analysis","score":0.1597999930381775,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.12060000002384186,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6517000198364258},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.6026999950408936},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5684999823570251},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.49720001220703125},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4514999985694885},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42660000920295715},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4207000136375427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6786999702453613},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6517000198364258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614799976348877},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.6026999950408936},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.49720001220703125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49239999055862427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.475600004196167},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4207000136375427},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.36480000615119934},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.33070001006126404},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.3294999897480011},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01163","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01163","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.2603.01163","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01163","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Face":[0],"retouching":[1,67,93,108,172],"requires":[2],"removing":[3],"subtle":[4,119],"imperfections":[5],"while":[6,47,159],"preserving":[7],"unique":[8],"facial":[9],"identity":[10],"features,":[11],"in":[12],"order":[13],"to":[14,34,39,75],"enhance":[15],"overall":[16,188],"aesthetic":[17,44,96,195],"appeal.":[18],"However,":[19],"existing":[20],"methods":[21,173],"suffer":[22],"from":[23],"a":[24,86,101,112,148],"fundamental":[25],"trade-off.":[26],"Supervised":[27],"learning":[28,50,88],"on":[29],"labeled":[30],"data":[31],"is":[32],"constrained":[33],"pixel-level":[35],"label":[36],"mimicry,":[37],"failing":[38],"capture":[40],"complex":[41],"subjective":[42],"human":[43,95,194],"preferences.":[45,97,196],"Conversely,":[46],"online":[48],"reinforcement":[49,87],"(RL)":[51],"excels":[52],"at":[53,151],"preference":[54,103],"alignment,":[55],"its":[56],"stochastic":[57,77,136,157],"exploration":[58,124],"paradigm":[59],"conflicts":[60],"with":[61,94,193],"the":[62,135],"high-fidelity":[63],"demands":[64],"of":[65,117],"face":[66,92,171],"and":[68,110,125,146,174,187],"often":[69],"introduces":[70],"noticeable":[71],"noise":[72],"artifacts":[73],"due":[74],"accumulated":[76],"drift.":[78],"To":[79,122],"address":[80],"these":[81],"limitations,":[82],"we":[83,127],"propose":[84],"BeautyGRPO,":[85],"framework":[89],"that":[90,166,190],"aligns":[91],"We":[98],"construct":[99],"FRPref-10K,":[100],"fine-grained":[102],"dataset":[104],"covering":[105],"five":[106],"key":[107],"dimensions,":[109],"train":[111],"specialized":[113,170],"reward":[114],"model":[115],"capable":[116],"evaluating":[118],"perceptual":[120],"differences.":[121],"reconcile":[123],"fidelity,":[126],"introduce":[128],"Dynamic":[129],"Path":[130],"Guidance":[131],"(DPG).":[132],"DPG":[133],"stabilizes":[134],"sampling":[137,153],"trajectory":[138,150],"by":[139],"dynamically":[140],"computing":[141],"an":[142],"anchor-based":[143],"ODE":[144],"path":[145],"replanning":[147],"guided":[149],"each":[152],"timestep,":[154],"effectively":[155],"correcting":[156],"drift":[158],"maintaining":[160],"controlled":[161],"exploration.":[162],"Extensive":[163],"experiments":[164],"show":[165],"BeautyGRPO":[167],"outperforms":[168],"both":[169],"general":[175],"image":[176],"editing":[177],"models,":[178],"achieving":[179],"superior":[180],"texture":[181],"quality,":[182],"more":[183],"accurate":[184],"blemish":[185],"removal,":[186],"results":[189],"better":[191],"align":[192]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
