{"id":"https://openalex.org/W7164939973","doi":"https://doi.org/10.48550/arxiv.2606.14759","title":"Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling","display_name":"Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164939973","doi":"https://doi.org/10.48550/arxiv.2606.14759"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14759","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.2606.14759","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103261392","display_name":"Y Cao","orcid":"https://orcid.org/0009-0001-5271-0894"},"institutions":[{"id":"https://openalex.org/I4210127738","display_name":"IMT Mines Al\u00e8s","ror":"https://ror.org/03e8rf594","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210127738"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Cao, Yiheng","raw_affiliation_strings":["SyCoIA - IMT Mines Al\u00e8s"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SyCoIA - IMT Mines Al\u00e8s","institution_ids":["https://openalex.org/I4210127738"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136591162","display_name":"Gustavo Andrade-Miranda","orcid":null},"institutions":[{"id":"https://openalex.org/I4210127738","display_name":"IMT Mines Al\u00e8s","ror":"https://ror.org/03e8rf594","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210127738"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Andrade-Miranda, Gustavo","raw_affiliation_strings":["SyCoIA - IMT Mines Al\u00e8s"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SyCoIA - IMT Mines Al\u00e8s","institution_ids":["https://openalex.org/I4210127738"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102970175","display_name":"Jiatian Zhang","orcid":"https://orcid.org/0009-0006-0257-8932"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiatian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138705875","display_name":"Guillaume Sall\u00e9","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sall\u00e9, Guillaume","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103668258","display_name":"Gao X","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":0.8313000202178955,"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":0.8313000202178955,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.014800000004470348,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.014399999752640724,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.5432999730110168},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5371999740600586},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5092999935150146},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.46480000019073486},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.42500001192092896},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4032999873161316},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3824000060558319},{"id":"https://openalex.org/keywords/spatial-coherence","display_name":"Spatial coherence","score":0.37529999017715454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3734999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5990999937057495},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5432999730110168},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5371999740600586},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5357999801635742},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4875999987125397},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.42500001192092896},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3824000060558319},{"id":"https://openalex.org/C2985909886","wikidata":"https://www.wikidata.org/wiki/Q193147","display_name":"Spatial coherence","level":3,"score":0.37529999017715454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C2776127602","wikidata":"https://www.wikidata.org/wiki/Q5038319","display_name":"Cardiac imaging","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C99398487","wikidata":"https://www.wikidata.org/wiki/Q257319","display_name":"Cardiac cycle","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C2987145844","wikidata":"https://www.wikidata.org/wiki/Q5038325","display_name":"Cardiac magnetic resonance","level":3,"score":0.35249999165534973},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C2776008845","wikidata":"https://www.wikidata.org/wiki/Q5038325","display_name":"Cardiac magnetic resonance imaging","level":3,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14759","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.2606.14759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14759","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cine":[0],"cardiac":[1,10,41,47,78,84],"magnetic":[2],"resonance":[3],"is":[4],"the":[5,13,19,82],"gold":[6],"standard":[7],"for":[8,34,115,124],"assessing":[9],"function,":[11],"but":[12],"scarcity":[14],"of":[15,21,113,122],"public":[16],"datasets":[17],"limits":[18],"development":[20],"advanced":[22],"data-driven":[23],"models.":[24],"To":[25],"address":[26],"this":[27],"limitation,":[28],"we":[29],"propose":[30],"a":[31,54,63,71,77,111,119,140],"generative":[32],"method":[33],"synthesizing":[35],"temporally":[36],"coherent":[37],"and":[38,89,96,104,118],"anatomically":[39,95],"consistent":[40],"sequences.":[42],"Our":[43,92],"text-to-video":[44],"framework":[45],"decouples":[46],"spatial":[48,87],"structure":[49],"from":[50,62],"temporal":[51,90,102],"motion.":[52],"First,":[53],"fine-tuned":[55],"diffusion":[56],"model":[57,74,93],"synthesizes":[58],"an":[59],"initial":[60],"frame":[61],"clinical":[64],"text":[65],"prompt,":[66],"controlling":[67],"anatomical":[68],"features.":[69],"Then,":[70],"latent":[72],"flow":[73],"conditioned":[75],"on":[76],"phase":[79],"embedding":[80],"generates":[81,94],"complete":[83],"motion,":[85],"ensuring":[86],"consistency":[88],"control.":[91],"pathologically":[97],"diverse":[98],"sequences":[99],"with":[100],"high":[101],"coherence":[103],"strong":[105],"fidelity":[106],"to":[107,133,143],"input":[108],"prompts,":[109],"achieving":[110],"FID":[112],"31.68":[114],"image":[116],"realism":[117],"CLIP":[120],"score":[121],"31.04":[123],"text-image":[125],"alignment.":[126],"These":[127],"experimental":[128],"results":[129],"highlight":[130],"its":[131],"potential":[132],"produce":[134],"high-fidelity,":[135],"on-demand":[136],"medical":[137],"data,":[138],"offering":[139],"scalable":[141],"solution":[142],"data":[144],"scarcity.":[145]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}