{"id":"https://openalex.org/W7151935438","doi":"https://doi.org/10.48550/arxiv.2604.04953","title":"Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity","display_name":"Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7151935438","doi":"https://doi.org/10.48550/arxiv.2604.04953"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04953","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04953","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.04953","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120710737","display_name":"Abhishek Dharmaratnakar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dharmaratnakar, Abhishek","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112264148","display_name":"Srikanth Ranganathan","orcid":"https://orcid.org/0009-0008-1358-2974"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ranganathan, Srivaths","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100584724","display_name":"Debanshu Das","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Debanshu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072410614","display_name":"Anushree Sinha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sinha, Anushree","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5120710737"],"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":0.3871000111103058,"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":0.3871000111103058,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.23980000615119934,"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/T11439","display_name":"Video Analysis and Summarization","score":0.11020000278949738,"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/generative-grammar","display_name":"Generative grammar","score":0.6200000047683716},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.5961999893188477},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46209999918937683},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4066999852657318},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.39719998836517334},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.37860000133514404},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3720000088214874},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.33230000734329224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7268000245094299},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6169999837875366},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.5961999893188477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47600001096725464},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3720000088214874},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.3314000070095062},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31790000200271606},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28600001335144043},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04953","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04953","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.04953","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04953","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":"article"},"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":{"The":[0],"domain":[1],"of":[2,80,124,162,183],"automatic":[3],"video":[4,55,169],"trailer":[5,157],"generation":[6,158],"is":[7],"currently":[8],"undergoing":[9],"a":[10,76,84,152],"profound":[11],"paradigm":[12],"shift,":[13],"transitioning":[14],"from":[15,110,146],"heuristic-based":[16],"extraction":[17],"methods":[18],"to":[19,37,115],"deep":[20],"generative":[21,88,178],"synthesis.":[22,142],"While":[23],"early":[24],"methodologies":[25],"relied":[26],"heavily":[27],"on":[28,87,128],"low-level":[29],"feature":[30],"engineering,":[31],"visual":[32],"saliency,":[33],"and":[34,53,95,102,133,180],"rule-based":[35],"heuristics":[36],"select":[38],"representative":[39],"shots,":[40],"recent":[41,147],"advancements":[42],"in":[43,159],"Large":[44,49],"Language":[45,50],"Models":[46,51],"(LLMs),":[47],"Multimodal":[48],"(MLLMs),":[52],"diffusion-based":[54],"synthesis":[56],"have":[57],"enabled":[58],"systems":[59,170],"that":[60,166],"not":[61],"only":[62],"identify":[63],"key":[64],"moments":[65],"but":[66],"also":[67],"construct":[68],"coherent,":[69],"emotionally":[70],"resonant":[71],"narratives.":[72],"This":[73],"survey":[74],"provides":[75],"comprehensive":[77],"technical":[78],"review":[79],"this":[81,149],"evolution,":[82],"with":[83],"specific":[85],"focus":[86],"techniques":[89],"including":[90],"autoregressive":[91],"Transformers,":[92],"LLM-orchestrated":[93],"pipelines,":[94],"text-to-video":[96],"foundation":[97,163],"models":[98],"like":[99],"OpenAI's":[100],"Sora":[101],"Google's":[103],"Veo.":[104],"We":[105],"analyze":[106],"the":[107,121,135,160],"architectural":[108],"progression":[109],"Graph":[111],"Convolutional":[112],"Networks":[113],"(GCNs)":[114],"Trailer":[116],"Generation":[117],"Transformers":[118],"(TGT),":[119],"evaluate":[120],"economic":[122],"implications":[123],"automated":[125],"content":[126],"velocity":[127],"User-Generated":[129],"Content":[130],"(UGC)":[131],"platforms,":[132],"discuss":[134],"ethical":[136],"challenges":[137],"posed":[138],"by":[139],"high-fidelity":[140],"neural":[141],"By":[143],"synthesizing":[144],"insights":[145],"literature,":[148],"report":[150],"establishes":[151],"new":[153],"taxonomy":[154],"for":[155],"AI-driven":[156],"era":[161],"models,":[164],"suggesting":[165],"future":[167],"promotional":[168],"will":[171],"move":[172],"beyond":[173],"extractive":[174],"selection":[175],"toward":[176],"controllable":[177],"editing":[179],"semantic":[181],"reconstruction":[182],"trailers.":[184]},"counts_by_year":[],"updated_date":"2026-04-09T06:13:59.934233","created_date":"2026-04-09T00:00:00"}