{"id":"https://openalex.org/W4407245190","doi":"https://doi.org/10.48550/arxiv.2502.04050","title":"PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models","display_name":"PartEdit: Fine-Grained Image Editing using Pre-Trained Diffusion Models","publication_year":2025,"publication_date":"2025-02-06","ids":{"openalex":"https://openalex.org/W4407245190","doi":"https://doi.org/10.48550/arxiv.2502.04050"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2502.04050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.04050","pdf_url":"https://arxiv.org/pdf/2502.04050","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":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2502.04050","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050147378","display_name":"Aleksandar Cveji\u0107","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cvejic, Aleksandar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033942292","display_name":"Abdelrahman Eldesokey","orcid":"https://orcid.org/0000-0003-3292-7153"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eldesokey, Abdelrahman","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059559749","display_name":"Peter Wonka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wonka, Peter","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050147378"],"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9215999841690063,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9215999841690063,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.6264587640762329},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6059470772743225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5740680694580078},{"id":"https://openalex.org/keywords/image-editing","display_name":"Image editing","score":0.5203108787536621},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.5182626247406006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.401775598526001},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3780277967453003},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09246227145195007},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.05945304036140442}],"concepts":[{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.6264587640762329},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6059470772743225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5740680694580078},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.5203108787536621},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.5182626247406006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.401775598526001},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3780277967453003},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09246227145195007},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.05945304036140442}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2502.04050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.04050","pdf_url":"https://arxiv.org/pdf/2502.04050","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":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2502.04050","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.04050","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":"pmh:oai:arXiv.org:2502.04050","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.04050","pdf_url":"https://arxiv.org/pdf/2502.04050","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":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4319453795","https://openalex.org/W4205104112","https://openalex.org/W4406058807","https://openalex.org/W1974870326","https://openalex.org/W2770776392","https://openalex.org/W3176454756","https://openalex.org/W2772330423","https://openalex.org/W3163523050","https://openalex.org/W3006381446","https://openalex.org/W3015353979"],"abstract_inverted_index":{"We":[0,81],"present":[1],"the":[2,22,61,116,131,168],"first":[3],"text-based":[4],"image":[5,17,29],"editing":[6,18,117,156],"approach":[7,153],"for":[8,146],"object":[9,46,73,93],"parts":[10,94],"based":[11],"on":[12,21,158],"pre-trained":[13,64],"diffusion":[14,26,39,65],"models.":[15],"Diffusion-based":[16],"approaches":[19],"capitalized":[20],"deep":[23],"understanding":[24,43],"of":[25,28,35,44,63,167],"models":[27,40,66],"semantics":[30],"to":[31,59,67,70,77,91,105,114,129],"perform":[32,78],"a":[33,140],"variety":[34],"edits.":[36,80],"However,":[37],"existing":[38,155],"lack":[41],"sufficient":[42],"many":[45],"parts,":[47,74],"hindering":[48],"fine-grained":[49,79],"edits":[50,132],"requested":[51],"by":[52,84,164],"users.":[53],"To":[54,134],"address":[55],"this,":[56],"we":[57,122,138],"propose":[58],"expand":[60],"knowledge":[62],"allow":[68],"them":[69,76],"understand":[71],"various":[72],"enabling":[75],"achieve":[82],"this":[83],"learning":[85],"special":[86],"textual":[87],"tokens":[88,102],"that":[89,151],"correspond":[90],"different":[92],"through":[95],"an":[96,143],"efficient":[97],"token":[98],"optimization":[99],"process.":[100],"These":[101],"are":[103],"optimized":[104],"produce":[106],"reliable":[107],"localization":[108],"masks":[109],"at":[110],"each":[111],"inference":[112],"step":[113],"localize":[115],"region.":[118],"Leveraging":[119],"these":[120],"masks,":[121],"design":[123],"feature-blending":[124],"and":[125,142,161],"adaptive":[126],"thresholding":[127],"strategies":[128],"execute":[130],"seamlessly.":[133],"evaluate":[135],"our":[136,152],"approach,":[137],"establish":[139],"benchmark":[141],"evaluation":[144],"protocol":[145],"part":[147],"editing.":[148],"Experiments":[149],"show":[150],"outperforms":[154],"methods":[157],"all":[159],"metrics":[160],"is":[162],"preferred":[163],"users":[165],"66-90%":[166],"time":[169],"in":[170],"conducted":[171],"user":[172],"studies.":[173]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
