[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81738-en":3,"doc-seo-81738-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81738,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Vertigo Vertigo Reconstructing a Cinematic Ideal through its Predictive AI Double","Vertigo Vertigo presents a scene-for-scene AI reconstruction of Alfred Hitchcock’s Vertigo (1958) created from only 2.78% of the original frames. The system extracts sparse keyframe anchors and applies first-last frame interpolation using a large video diffusion model to predict intervening sequences. Computational evaluation and critical feedback show 73.1% structural recognizability, with only 3.6% catastrophic failures. The unstable overlay between original and predictive shadow produces a modern “vertigo,” arguing generative media accelerates classical cinema’s logic of desire and false authenticity.","Vertigo Vertigo: Reconstructing a Cinematic Ideal through its  \nPredictive AI Double  \nAdam Cole  \n[a.cole@arts.ac.uk](a.cole@arts.ac.uk)[ ](a.cole@arts.ac.uk)University ofthe Arts London London, UK  \nMick Grierson  \n[m.grierson@arts.ac.uk](m.grierson@arts.ac.uk)[ ](m.grierson@arts.ac.uk)University ofthe Arts London London, UK  \narXiv :2607 .00047v2 [ cs .MM] 7 Jul 2026  \nFigure 1: Vertigo Vertigo (2026): an AI reconstruction of Hitchcock’s Vertigo built from 2.78% of the original film’s frames, here showing the original overlaid with its predictive shadow.  \nAbstract  \nVertigo Vertigo is a scene-for-scene AI reconstruction of Hitchcock’s Vertigo (1958), generated from only 2.78% of the original film’s frames. Using this sparse set of keyframe anchors, we perform firstlast frame interpolation via a large video diffusion model to predict the intervening sequences. Vertigo is itself a film about the obsessive reconstruction of an artificial ideal; Vertigo Vertigo extends this logic to the material of the film, treating the canonical text as a probe for the normative conventions of classical cinema encoded within generative systems. Evaluated through computational analysis and critical feedback from media theorists (Lev Manovich, Shane Denson, Kevin L. Ferguson), the artifact demonstrates remarkable structural fidelity: 73.1% of frames are recognizable as plausible renditions of Vertigo and only 3.6% fail catastrophically. This fidelity suggests that cinematic norms are deeply compressed within the model’s latent priors. Aesthetically, the reconstruction is rendered as an unstable overlay between the original film and its predictive  \nshadow, fueling a persistent doubt in the viewer’s perception of authenticity — a 21st-century vertigo. The work argues that generative media is not a paradigm shift from cinema but an acceleration of its logic of desire and false authenticity, extending from classical Hollywood through to the predictive media environments now reshaping contemporary perception.  \nCCS Concepts  \n• Applied computing → Media arts; • Computing methodologies → Computer vision representations; Computer graphics.  \nKeywords  \nAI Video, Diffusion Models, Computational Film Theory, PostCinema, Experimental Video Art  \nAdam Cole and Mick Grierson  \n1 Introduction  \n“I need you to be Madeleine for a while,” Scottie famously demands of Judy in the final act of Alfred Hitchcock’s Vertigo (1958) . In this classic Hollywood film, a retired detective becomes dangerously obsessed with Madeleine, a woman he was hired to follow. After her apparent death, he remolds a new acquaintance, Judy, by meticulously contorting her appearance and behavior into the exact image of this lost love — an image that, it turns out, was an artificial persona. This fixation on constructing a desired ideal finds a modern parallel in our interactions with generative AI: just as Scottie chases a perfected simulation, the contemporary culture of prompt-based AI, where one might literally type “I need you to be”into a machine, defines a relationship in which human desire and algorithmic conformity converge in the act of conception.  \nVertigo Vertigo is an experimental video artwork that plays with the original film’s recursive logic of constructed identity and voyeuristic desire through a scene-for-scene AI remake of the original work. Utilizing a small fraction of the source footage, Vertigo Vertigo applies this same logic of obsessive reconstruction to the material of the film itself. Specifically, the project algorithmically extracts keyframes amounting to 2.78% of the source material and utilizes a 14-billion-parameter image-to-video diffusion model to run firstlast frame interpolation between them, filling in the blanks of the intermediary spaces. The resulting film, equal in length to Vertigo, physically overlaps with the original at its keyframe anchor points but constantly diverges in the spaces between, creating an entirely hypothetical yet plausible ","cbCaiily1oX7zJFy","https://ap.wps.com/l/cbCaiily1oX7zJFy","pdf",7418212,1,7,"English","en",105,"# Introduction\n## Generative Video and the Discorrelated Image\n# Methodology\n## Keyframe extraction and interpolation\n# Evaluation\n## Quantitative fidelity and failure analysis\n# Discussion","[{\"question\":\"How is Vertigo Vertigo reconstructed from the original film?\",\"answer\":\"The project extracts keyframes totaling 2.78% of the source material and uses first-last frame interpolation with a large video diffusion model to generate the missing intermediary sequences scene by scene.\"},{\"question\":\"What evaluation results indicate how faithful the reconstruction is?\",\"answer\":\"Computational analysis finds 73.1% of frames recognizable as plausible renditions, while only 3.6% fail catastrophically. Critical feedback supports the structural fidelity and perceptual effects of the overlay.\"},{\"question\":\"What is the main artistic and theoretical argument of the work?\",\"answer\":\"The project treats the canonical film as a probe for normative classical-cinema conventions encoded within generative systems, showing how compressed cinematic norms in latent priors create “false authenticity” and perceptual doubt.\"}]",1784175754,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"vertigo-vertigo-reconstructing-a-cinematic-ideal-through-its-predictive-ai-double","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/vertigo-vertigo-reconstructing-a-cinematic-ideal-through-its-predictive-ai-double/81738/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is Vertigo Vertigo reconstructed from the original film?","Question",{"text":75,"@type":76},"The project extracts keyframes totaling 2.78% of the source material and uses first-last frame interpolation with a large video diffusion model to generate the missing intermediary sequences scene by scene.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What evaluation results indicate how faithful the reconstruction is?",{"text":80,"@type":76},"Computational analysis finds 73.1% of frames recognizable as plausible renditions, while only 3.6% fail catastrophically. Critical feedback supports the structural fidelity and perceptual effects of the overlay.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main artistic and theoretical argument of the work?",{"text":84,"@type":76},"The project treats the canonical film as a probe for normative classical-cinema conventions encoded within generative systems, showing how compressed cinematic norms in latent priors create “false authenticity” and perceptual doubt.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]