{"id":"https://openalex.org/W7164920117","doi":"https://doi.org/10.48550/arxiv.2606.15592","title":"DenseControl: Instance-Level Controllable Synthesis of Dense Crowd Image","display_name":"DenseControl: Instance-Level Controllable Synthesis of Dense Crowd Image","publication_year":2026,"publication_date":"2026-06-14","ids":{"openalex":"https://openalex.org/W7164920117","doi":"https://doi.org/10.48550/arxiv.2606.15592"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15592","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15592","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.15592","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138744332","display_name":"Juncheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Juncheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076264344","display_name":"Lei Shang","orcid":"https://orcid.org/0000-0003-4395-3226"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364503","display_name":"Lu Wang","orcid":"https://orcid.org/0000-0002-9437-6898"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374253","display_name":"Bo Sun","orcid":"https://orcid.org/0000-0001-7001-8781"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Baigui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138735395","display_name":"Shujun Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shujun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5694000124931335,"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":0.5694000124931335,"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":0.09210000187158585,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.03579999879002571,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/embedding","display_name":"Embedding","score":0.7164000272750854},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5489000082015991},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5437999963760376},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5260000228881836},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.520799994468689},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.477400004863739},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45419999957084656},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4472000002861023},{"id":"https://openalex.org/keywords/codebase","display_name":"Codebase","score":0.44339999556541443},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4032000005245209}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7797999978065491},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7164000272750854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5569999814033508},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5489000082015991},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5437999963760376},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5260000228881836},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5145000219345093},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.477400004863739},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C51929080","wikidata":"https://www.wikidata.org/wiki/Q2425187","display_name":"Codebase","level":3,"score":0.44339999556541443},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4032000005245209},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27889999747276306},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15592","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15592","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.15592","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15592","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,35,79,107,139,181],"introduce":[4,81],"DenseControl,":[5],"a":[6,88,141],"novel":[7,89],"pipeline":[8],"for":[9,38,104],"generating":[10],"dense":[11,176],"crowd":[12,62,177,186],"images.":[13],"Specifically,":[14],"DenseControl":[15,51,154,171],"meticulously":[16],"positions":[17],"and":[18,30,44,67,160,193],"sizes":[19],"each":[20],"generated":[21],"instance":[22,73],"to":[23,122,148,197],"align":[24],"precisely":[25],"with":[26,101,118,135],"the":[27,41,54,82,98,119,130,136,199],"predefined":[28],"coordinates":[29],"scales.":[31],"Based":[32],"on":[33],"this,":[34],"further":[36,128],"allow":[37],"control":[39,95,168],"over":[40],"background,":[42],"style,":[43],"attributes":[45],"of":[46,56,132,202],"instances.":[47],"The":[48,204],"motivation":[49],"behind":[50],"stems":[52],"from":[53],"observation":[55],"two":[57,156],"main":[58],"challenges":[59],"in":[60,162,175,184],"synthesizing":[61],"images:":[63],"controlling":[64],"signal":[65],"embedding":[66],"maintaining":[68],"topological":[69],"integrity":[70],"when":[71],"imparting":[72],"scale":[74,125],"guidance.":[75],"To":[76,127],"address":[77],"these,":[78],"first":[80],"Isolated":[83],"Object":[84],"Embedding":[85,112],"(IOE)":[86],"map,":[87,138],"representation":[90],"that":[91,115,145],"facilitates":[92],"spatial":[93],"location":[94],"while":[96],"mitigating":[97],"difficulties":[99],"associated":[100],"learning":[102],"projections":[103],"model.":[105],"Secondly,":[106],"propose":[108],"an":[109],"Implicit":[110],"Scale":[111],"(ISE)":[113],"strategy":[114],"seamlessly":[116],"integrates":[117],"IOE":[120,137],"map":[121],"encode":[123],"precise":[124],"information.":[126],"enhance":[129],"efficacy":[131],"combining":[133],"ISE":[134],"incorporate":[140],"Position":[142],"Shortcut":[143],"mechanism":[144],"enhances":[146],"cross-attention":[147],"alleviate":[149],"projection":[150],"challenges.":[151],"We":[152],"evaluate":[153],"through":[155],"lenses:":[157],"synthesis":[158],"quality":[159],"applicability":[161],"latent":[163],"applications.":[164],"Experiments":[165],"across":[166],"different":[167],"conditions":[169],"demonstrate":[170],"achieves":[172],"state-of-the-art":[173],"results":[174],"image":[178],"synthesis.":[179],"Furthermore,":[180],"showcase":[182],"applications":[183],"augmenting":[185],"analysis":[187],"under":[188],"data":[189],"scarcity,":[190],"transfer":[191],"learning,":[192],"weather":[194],"generalization":[195],"scenes,":[196],"highlight":[198],"practical":[200],"utility":[201],"DenseControl.":[203],"codebase":[205],"will":[206],"be":[207],"released.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}