{"id":"https://openalex.org/W7171413918","doi":"https://doi.org/10.48550/arxiv.2607.21628","title":"Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation","display_name":"Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7171413918","doi":"https://doi.org/10.48550/arxiv.2607.21628"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.21628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.21628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.21628","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143743301","display_name":"Kaiwen Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Kaiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062955059","display_name":"Frank Bieder","orcid":"https://orcid.org/0009-0007-1178-4487"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bieder, Frank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085611540","display_name":"Yinzhe Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yinzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143703623","display_name":"Carlos Fernandez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fernandez, Carlos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020982793","display_name":"Jan-Hendrik Pauls","orcid":"https://orcid.org/0000-0003-2048-392X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pauls, Jan-Hendrik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122868826","display_name":"\u00d6mer \u015eahin Ta\u015f","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tas, Omer Sahin","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":0.9729999899864197,"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":0.9729999899864197,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.007000000216066837,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0035000001080334187,"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/wavelet","display_name":"Wavelet","score":0.4927999973297119},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.4699999988079071},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3815999925136566},{"id":"https://openalex.org/keywords/semantic-gap","display_name":"Semantic gap","score":0.3797000050544739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3434999883174896},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.33410000801086426},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.3280999958515167},{"id":"https://openalex.org/keywords/texture-synthesis","display_name":"Texture synthesis","score":0.3280999958515167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214000225067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5986999869346619},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5134999752044678},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.4699999988079071},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38749998807907104},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.3797000050544739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.33410000801086426},{"id":"https://openalex.org/C50494287","wikidata":"https://www.wikidata.org/wiki/Q658467","display_name":"Texture synthesis","level":5,"score":0.3280999958515167},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.32679998874664307},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C2908650547","wikidata":"https://www.wikidata.org/wiki/Q20999234","display_name":"Intrinsics","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C200873422","wikidata":"https://www.wikidata.org/wiki/Q5448821","display_name":"Filling-in","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.21628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.21628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.21628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.21628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"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":{"Simulation-to-reality":[0],"translation":[1],"must":[2],"bridge":[3],"the":[4,110,135,139,142,182,213],"appearance":[5,55,179],"gap":[6],"between":[7],"synthetic":[8,143],"and":[9,15,43,86,92,146,158,199,223,227],"real":[10],"domains":[11],"while":[12,181,202,218],"preserving":[13],"structural":[14,91,205],"semantic":[16,93,200],"consistency.":[17,94],"Conditioning-based":[18],"methods":[19,30,47,196,217],"achieve":[20,31],"spatial":[21,81,164],"alignment":[22],"but":[23,33,51,69],"introduce":[24,96,159],"computationally":[25],"expensive":[26],"control":[27],"modules.":[28],"Paired-data":[29],"realism":[32,198,214],"rely":[34],"on":[35,154],"complex":[36],"synthesis":[37],"pipelines,":[38],"often":[39],"altering":[40],"scene":[41,184],"geometry":[42],"semantics.":[44],"Training-free":[45],"editing":[46],"avoid":[48],"both":[49],"constraints":[50],"lack":[52],"a":[53,66],"learned":[54],"prior,":[56],"limiting":[57],"their":[58],"perceptual":[59],"quality.":[60],"Recently":[61],"proposed":[62],"phase-preserving":[63],"diffusion":[64],"presents":[65],"promising":[67],"alternative,":[68],"Fourier-domain":[70],"formulations":[71],"are":[72,175],"constrained":[73],"by":[74,225],"global":[75,127],"spectral":[76,128],"coupling.":[77],"This":[78],"coupling":[79],"induces":[80],"artifacts":[82],"such":[83],"as":[84],"ringing":[85],"boundary":[87],"leakage,":[88],"thereby":[89],"degrading":[90],"We":[95],"Wavelet":[97,113],"Phase":[98],"Diffusion,":[99],"which":[100],"addresses":[101],"this":[102],"through":[103],"two":[104],"components.":[105],"First,":[106],"we":[107],"operate":[108],"in":[109,197],"Dual-Tree":[111],"Complex":[112],"Packet":[114],"Transform":[115],"domain,":[116],"whose":[117],"localized":[118],"wavelet":[119],"packets":[120],"enable":[121],"spatially":[122],"adaptive":[123],"phase":[124],"injection":[125],"without":[126],"interference.":[129],"Second,":[130],"Low-Frequency":[131],"Randomization":[132],"(LFR)":[133],"replaces":[134],"low-frequency":[136],"packet,":[137],"decoupling":[138],"model":[140],"from":[141],"illumination":[144],"prior":[145,195],"enabling":[147],"in-distribution":[148],"real-world":[149],"appearance.":[150],"Both":[151],"components":[152],"train":[153],"unpaired":[155],"open-domain":[156],"data,":[157],"negligible":[160],"inference":[161],"overhead.":[162],"The":[163],"locality":[165],"further":[166],"enables":[167],"instance-level":[168],"translation,":[169,192,210],"where":[170],"individual":[171],"objects":[172],"or":[173],"regions":[174],"translated":[176],"to":[177],"photorealistic":[178],"independently":[180],"surrounding":[183],"remains":[185],"untranslated.":[186],"On":[187],"vKITTI":[188],"$\\to$":[189],"KITTI":[190],"image":[191],"ours":[193,211],"outperforms":[194],"consistency":[201],"maintaining":[203],"competitive":[204],"alignment.":[206],"For":[207],"CARLA":[208],"video":[209],"approaches":[212],"of":[215],"paired-data":[216],"reducing":[219],"VLM":[220],"planner":[221],"ADE":[222],"FDE":[224],"$5.4\\%$":[226],"$5.1\\%$,":[228],"respectively.":[229]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-28T00:00:00"}