Vertigo Vertigo: Reconstructing a Cinematic Ideal through its Predictive AI Double

2026-06-29

AI Summary

This study investigates the extent to which generative AI internalizes classical cinematic conventions by reconstructing Alfred Hitchcock’s *Vertigo* scene-by-scene using only 2.78% of its keyframes as input to a large video diffusion model, which interpolates between start and end frames. Integrating keyframe anchoring, computational analysis, and media-theoretical critique, the work extends the film’s theme of “artificial ideal reconstruction” to the ontological level of the medium itself, employing AI generation as a probe into the model’s mechanisms of compressing and reproducing cinematic language. Experimental results demonstrate that 73.1% of reconstructed frames were judged as plausible reconstructions, with only 3.6% exhibiting severe distortion, confirming that canonical filmic norms are deeply embedded within the model’s priors.

Abstract

Vertigo 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 first-last 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 shadow, 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.

Problem

• generative AI
• cinematic reconstruction
• classical cinema
• predictive media
• film fidelity

Innovation

• video diffusion model
• keyframe interpolation
• generative AI
• cinematic norms
• predictive media