{"id":"https://openalex.org/W4417125349","doi":"https://doi.org/10.1145/3757377.3763964","title":"LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization","display_name":"LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorization","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W4417125349","doi":"https://doi.org/10.1145/3757377.3763964"},"language":null,"primary_location":{"id":"doi:10.1145/3757377.3763964","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3757377.3763964","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SIGGRAPH Asia 2025 Conference Papers","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/A5065448412","display_name":"Ronghuan Wu","orcid":"https://orcid.org/0000-0001-9741-9876"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Ronghuan Wu","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036719284","display_name":"Wanchao Su","orcid":"https://orcid.org/0000-0002-7498-3033"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanchao Su","raw_affiliation_strings":["Monash University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013972536","display_name":"Jing Liao","orcid":"https://orcid.org/0000-0001-7014-5377"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jing Liao","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065448412"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":1.428,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87642706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6198999881744385,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6198999881744385,"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":0.09489999711513519,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.057999998331069946,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/vectorization","display_name":"Vectorization (mathematics)","score":0.942799985408783},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.8054999709129333},{"id":"https://openalex.org/keywords/vector-graphics","display_name":"Vector graphics","score":0.6384000182151794},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4708999991416931},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42899999022483826},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42089998722076416},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics","score":0.39879998564720154},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37470000982284546},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.34860000014305115}],"concepts":[{"id":"https://openalex.org/C41681595","wikidata":"https://www.wikidata.org/wiki/Q7917855","display_name":"Vectorization (mathematics)","level":2,"score":0.942799985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8450000286102295},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.8054999709129333},{"id":"https://openalex.org/C59662460","wikidata":"https://www.wikidata.org/wiki/Q170130","display_name":"Vector graphics","level":3,"score":0.6384000182151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.571399986743927},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49380001425743103},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4708999991416931},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.34860000014305115},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.32710000872612},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.28700000047683716},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27079999446868896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3757377.3763964","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3757377.3763964","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SIGGRAPH Asia 2025 Conference Papers","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/W1970221512","https://openalex.org/W1983062295","https://openalex.org/W2038825728","https://openalex.org/W2040555343","https://openalex.org/W2060716992","https://openalex.org/W2063155970","https://openalex.org/W2090159818","https://openalex.org/W2137722745","https://openalex.org/W2168919480","https://openalex.org/W2207928131","https://openalex.org/W2530658473","https://openalex.org/W2771008866","https://openalex.org/W2809859740","https://openalex.org/W2885149812","https://openalex.org/W2962785568","https://openalex.org/W3015634290","https://openalex.org/W3107232127","https://openalex.org/W3136398160","https://openalex.org/W3164412074","https://openalex.org/W4308550530","https://openalex.org/W4312457448","https://openalex.org/W4385283764","https://openalex.org/W4386076215","https://openalex.org/W4386076532","https://openalex.org/W4386113271","https://openalex.org/W4390872297","https://openalex.org/W4390873160","https://openalex.org/W4390873195","https://openalex.org/W4390873319","https://openalex.org/W4393149383","https://openalex.org/W4400818002","https://openalex.org/W4400820794","https://openalex.org/W4402716166","https://openalex.org/W4402753913","https://openalex.org/W4402753926","https://openalex.org/W4404526118","https://openalex.org/W4407762655","https://openalex.org/W4409697916","https://openalex.org/W4415799132"],"related_works":[],"abstract_inverted_index":{"Image":[0],"vectorization":[1,21,51,65,203],"is":[2],"a":[3,61,72,117,143,181],"powerful":[4],"technique":[5],"that":[6,32,67,120,162,196],"converts":[7],"raster":[8],"images":[9],"into":[10],"vector":[11,101],"graphics,":[12],"enabling":[13,126],"enhanced":[14],"flexibility":[15],"and":[16,41,53,89,106,129,192,211,216],"interactivity.":[17],"However,":[18],"popular":[19],"image":[20,44,64,145],"tools":[22],"struggle":[23],"with":[24,103,205],"occluded":[25],"regions,":[26],"producing":[27,202],"incomplete":[28],"or":[29],"fragmented":[30],"shapes":[31],"hinder":[33],"editability.":[34],"While":[35],"recent":[36],"advancements":[37],"have":[38],"explored":[39],"optimization-based":[40],"learning-based":[42],"layer-wise":[43,63],"vectorization,":[45],"these":[46,69],"methods":[47],"face":[48],"limitations":[49],"in":[50,82],"quality":[52],"flexibility.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58,99,157,179],"introduce":[59],"LayerPeeler,":[60],"novel":[62],"approach":[66],"addresses":[68],"challenges":[70],"through":[71],"progressive":[73],"simplification":[74],"paradigm.":[75],"The":[76],"key":[77],"to":[78,115,148,167],"LayerPeeler\u2019s":[79],"success":[80],"lies":[81],"its":[83],"autoregressive":[84],"peeling":[85,188],"strategy:":[86],"by":[87],"identifying":[88],"removing":[90],"the":[91,150,165,173],"topmost":[92],"non-occluded":[93,132],"layers":[94],"while":[95,170],"recovering":[96],"underlying":[97],"content,":[98],"generate":[100],"graphics":[102],"complete":[104],"paths":[105],"coherent":[107],"layer":[108,118,187],"structures.":[109],"Our":[110,214],"method":[111],"leverages":[112],"vision-language":[113],"models":[114],"construct":[116],"graph":[119],"captures":[121],"occlusion":[122],"relationships":[123],"among":[124],"elements,":[125],"precise":[127],"detection":[128],"description":[130],"for":[131,142,186],"layers.":[133,152],"These":[134],"descriptive":[135],"captions":[136],"are":[137],"used":[138],"as":[139],"editing":[140],"instructions":[141],"finetuned":[144],"diffusion":[146],"model":[147,166],"remove":[149],"identified":[151],"To":[153,176],"ensure":[154],"accurate":[155],"removal,":[156],"employ":[158],"localized":[159],"attention":[160],"control":[161],"precisely":[163],"guides":[164],"target":[168],"regions":[169],"faithfully":[171],"preserving":[172],"surrounding":[174],"content.":[175],"support":[177],"this,":[178],"contribute":[180],"large-scale":[182],"dataset":[183,217],"specifically":[184],"designed":[185],"tasks.":[189],"Extensive":[190],"quantitative":[191],"qualitative":[193],"experiments":[194],"demonstrate":[195],"LayerPeeler":[197],"significantly":[198],"outperforms":[199],"existing":[200],"techniques,":[201],"results":[204],"superior":[206],"path":[207],"semantics,":[208],"geometric":[209],"regularity,":[210],"visual":[212],"fidelity.":[213],"code":[215],"will":[218],"be":[219],"available":[220],"at":[221],"https://layerpeeler.github.io/.":[222]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-08T00:00:00"}