{"id":"https://openalex.org/W7163923788","doi":"https://doi.org/10.48550/arxiv.2606.07053","title":"TrioPose: Native Triple-Stream Diffusion Transformers for Pose-Guided Text-to-Image Generation","display_name":"TrioPose: Native Triple-Stream Diffusion Transformers for Pose-Guided Text-to-Image Generation","publication_year":2026,"publication_date":"2026-06-05","ids":{"openalex":"https://openalex.org/W7163923788","doi":"https://doi.org/10.48550/arxiv.2606.07053"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07053","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07053","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.07053","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138140538","display_name":"Dian Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Dian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5019383391","display_name":"Zhengyi Yang","orcid":"https://orcid.org/0000-0003-1772-6863"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhengyi","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":0.9032999873161316,"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":0.9032999873161316,"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/T11349","display_name":"Music Technology and Sound Studies","score":0.01269999984651804,"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":0.011800000444054604,"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/concatenation","display_name":"Concatenation (mathematics)","score":0.6276000142097473},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.6079999804496765},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4936000108718872},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.48010000586509705},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.421999990940094},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.3693000078201294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3427000045776367}],"concepts":[{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.6276000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6116999983787537},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.6079999804496765},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4936000108718872},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4205999970436096},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3767000138759613},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C19743564","wikidata":"https://www.wikidata.org/wiki/Q25378119","display_name":"Flicker","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C169822122","wikidata":"https://www.wikidata.org/wiki/Q230187","display_name":"Crosstalk","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25380000472068787},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07053","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07053","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07053","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07053","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.46269482374191284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pose-guided":[0],"text-to-image":[1],"generation":[2],"often":[3],"suffers":[4],"from":[5],"limb":[6],"distortions":[7],"and":[8,78,161,186],"feature":[9],"crosstalk":[10],"in":[11,37,190],"complex":[12,191],"multi-person":[13],"scenarios.":[14],"While":[15],"existing":[16],"UNet-based":[17],"adapters":[18],"struggle":[19],"with":[20],"long-range":[21],"spatial":[22],"dependencies,":[23],"emerging":[24],"Multimodal":[25],"Diffusion":[26],"Transformers":[27],"(MM-DiTs)":[28],"offer":[29],"superior":[30],"global":[31],"modeling.":[32],"However,":[33],"naive":[34],"signal":[35],"concatenation":[36],"MM-DiTs":[38],"severely":[39],"disrupts":[40],"pre-trained":[41,89],"latent":[42,90],"distributions.":[43],"To":[44,92],"address":[45],"this,":[46],"we":[47,60,97],"propose":[48],"TrioPose,":[49],"a":[50,62,99,125,173],"native":[51,133],"pose-driven":[52],"framework":[53],"built":[54],"upon":[55],"the":[56,132],"SD3.5M":[57],"architecture.":[58],"Specifically,":[59],"introduce":[61],"Triple-Stream":[63],"Pose-Aware":[64],"DiT":[65],"(TSPA-DiT)":[66],"that":[67,104,150],"treats":[68],"pose":[69],"as":[70],"an":[71,166],"independent":[72],"modality.":[73],"It":[74],"employs":[75],"layer-wise":[76],"activation":[77],"zero-initialized":[79],"dual-residual":[80],"injection":[81],"to":[82,119],"smoothly":[83],"enforce":[84],"geometric":[85],"constraints":[86,118],"while":[87,179],"preserving":[88],"stability.":[91],"resolve":[93],"severe":[94],"multi-instance":[95],"occlusions,":[96],"design":[98],"Learnable":[100],"Relational":[101],"Bias":[102],"Mask":[103],"categorizes":[105],"topological":[106],"connectivity":[107],"into":[108,114],"fine-grained":[109],"physical":[110],"states,":[111],"mapping":[112],"them":[113],"continuous":[115],"attention":[116],"soft":[117],"effectively":[120],"decouple":[121],"inter-instance":[122],"interference.":[123],"Furthermore,":[124],"Pose-Guided":[126],"Spatial":[127],"Loss":[128],"Weighting":[129],"strategy":[130],"modulates":[131],"diffusion":[134],"objective":[135],"using":[136],"heatmap-derived":[137],"error":[138],"maps,":[139],"focusing":[140],"anatomical":[141],"supervision":[142],"strictly":[143],"on":[144,170],"distortion-prone":[145],"regions.":[146],"Extensive":[147],"experiments":[148],"demonstrate":[149],"TrioPose":[151],"achieves":[152],"state-of-the-art":[153],"performance":[154],"across":[155],"challenging":[156],"benchmarks,":[157],"including":[158],"Human-Art,":[159,171],"CrowdPose,":[160],"OCHuman.":[162],"Notably,":[163],"it":[164],"attains":[165],"AP":[167],"of":[168],"$64.33$":[169],"representing":[172],"$30\\%$":[174],"improvement":[175],"over":[176],"prior":[177],"arts,":[178],"setting":[180],"new":[181],"standards":[182],"for":[183],"visual":[184],"fidelity":[185],"text-image":[187],"semantic":[188],"alignment":[189],"multi-human":[192],"generation.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-09T00:00:00"}
