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Other literature type . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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When Cinema Thinks Through Form: Anamorphic, Optical Imperfection and Filmic Language in Generative Cinema

Authors: Cristallo Scalzi, Carmine;

When Cinema Thinks Through Form: Anamorphic, Optical Imperfection and Filmic Language in Generative Cinema

Abstract

Contemporary generative image systems increasingly equate visual quality with total visibility: ultra-high resolution, uniform sharpness, noise suppression, and spatial coherence. In parallel, streaming distribution has normalized a video-oriented aesthetic optimized for domestic displays, contributing to the erosion of filmic texture, ambiguity, and perceptual depth. This paper argues that anamorphic cinema offers a paradigmatic counter-model to this tendency, not as a stylistic preference but as an epistemological case in which optical imperfection functions as intentional cinematic language. Characteristics such as oval bokeh, edge distortion, spatial compression, and non-uniform sharpness are examined as structural devices through which cinema organizes space, attention, and meaning. Starting from anamorphic cinema as a primary case study, the paper extends the discussion to other coherent formal strategies—including constrained aspect ratios (e.g. 4:3), luminance-driven black-and-white regimes, shallow depth of field, and film-oriented color science—situating them within a unified ethical framework of cinematic form. In the context of generative cinema, the paper critiques the normalization bias of diffusion-based models and proposes an author-driven, ethically constrained generative pipeline, arguing that while powerful stories determine the images they require, it is the rigor of the chosen image-form that allows cinematic works to endure.

Keywords

Anamorphic cinema,, datasets, Artificial intelligence and cinema, Generative cinema, Diffusion models, Cinematic language, LoRA training

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average