Abstract

This article introduces AI Cinematic Realism, a theoretical and creative framework that reconceptualizes realism in the era of generative artificial intelligence. Rejecting the long-standing assumption that cinematic realism depends on photographic indexicality, the framework argues that realism is better understood as a cognitive and affective event co-produced by human intention and machine generativity. Drawing on extended mind theory (Clark & Chalmers, 1998), the article positions cinema as a distributed cognitive system in which tools, representations, and viewers collaboratively construct meaning. AI-generated images, though non-photographic, achieve cinematic force through four emergent properties—temporal implication, spatial coherence, character interiority, and atmospheric continuity—which together produce emotional plausibility, a new index of realism appropriate to synthetic media. Situating AI Cinematic Realism within a lineage of realist refusals, from Italian Neorealism to Dogme 95, the article argues that each realist movement redefines realism by breaking with the dominant spectacle of its era. AI Cinematic Realism extends this trajectory by rejecting the contemporary fetish for photorealistic simulation, offering instead a model of realism grounded in perceptual coherence, ethical co-authorship, and the atmospheric logic of scenes. The framework builds on concepts first articulated in AI Cinematic Realism (Gutierrez, 2026), expanding them into a comprehensive theory of realism after photography.

Keywords: AI cinema, realism, synthetic images, emotional plausibility, film theory, extended mind

Introduction

The rapid proliferation of generative AI has unsettled long-standing assumptions about what constitutes a cinematic image. For over a century, film theory has anchored realism in the camera’s indexical bond to the physical world. Even as digital cinema eroded this bond, the ideal of photographic realism persisted as a normative horizon. Today, however, AI-generated images—produced without cameras, sets, or physical referents—challenge the very ontology of cinematic representation.

This article argues that the emergence of synthetic images does not signal the end of realism but rather its transformation. AI Cinematic Realism offers a framework for understanding how AI-generated images can produce cinematic realism without relying on photographic indexicality. Instead, realism emerges from the behavior of images: their temporal, spatial, affective, and atmospheric coherence.

The central claim is simple but radical: realism is not a property of the image but a property of the viewer’s cognitive engagement with the image (Gutierrez, 2026). AI, far from undermining realism, reveals this truth with unprecedented clarity.

Cinema as a Distributed Cognitive System

Extended Mind Theory and the Cinematic Image

Extended mind theory argues that cognition is not confined to the brain but distributed across tools, environments, and representational systems (Clark & Chalmers, 1998). Cinema, from its inception, has functioned as such a system—an external cognitive apparatus that shapes perception, memory, and affect.

AI intensifies this dynamic. Generative models do not merely record the world; they participate in the construction of possible worlds. They externalize aspects of imagination, inference, and narrative sense-making. In this view, AI-generated images are not deviations from realism but extensions of the cognitive processes that make realism possible (Gutierrez, 2026).

Realism Beyond Indexicality

Classical realist theorists such as André Bazin and Siegfried Kracauer grounded realism in the camera’s capacity to reveal the world. AI Cinematic Realism departs from this tradition by shifting the locus of realism from the origin of the image to the experience of the image.

Realism becomes:

• a perceptual effect
• an affective resonance
• a cognitive inference
• a collaborative construction

This shift aligns realism with contemporary understandings of perception as inferential and predictive rather than passive and photographic.

The Lineage of Realist Refusals

Every major realist movement has defined itself through refusal—rejecting the dominant spectacle of its era in order to reclaim a more grounded mode of representation.

Movement	What It Refused	What It Sought
Italian Neorealism	Studio artifice	Everyday truth
Dogme 95	Technical excess	Purity and immediacy
AI Cinematic Realism	Photographic fetishism, hyper-simulation	Emotional plausibility and cognitive coherence

AI Cinematic Realism continues this lineage by refusing the contemporary obsession with photorealistic simulation (Gutierrez, 2026). Instead, it proposes a realism grounded in the logic of scenes rather than the look of scenes.

The Four Pillars of AI Cinematic Realism

Temporal Implication

Cinematic images imply duration. A single frame suggests a before and after. AI images achieve temporal implication when they contain:

• traces of prior action
• tensions that anticipate future events
• gestures that feel mid-movement

Temporal implication transforms a static image into a cinematic moment.

Spatial Coherence

Cinematic worlds feel navigable. Spatial coherence arises when:

• environments obey consistent internal logic
• objects relate plausibly to one another
• depth, scale, and atmosphere align

AI models, when guided with intentionality, can produce spaces that feel lived-in rather than assembled.

Character Interiority

Cinematic characters possess inner lives. AI images achieve interiority when:

• expressions imply thought
• posture suggests motivation
• the image invites psychological inference

Interiority is not a property of the face but of the viewer’s engagement with the face.

Atmospheric Continuity

Atmosphere binds a scene together. It is the mood, tone, and sensorial coherence that makes a world feel whole. AI-generated images achieve atmospheric continuity when:

• lighting, color, and texture align
• emotional tone is consistent
• the image feels like part of a larger world

Atmosphere is the glue of cinematic realism.

Emotional Plausibility: A New Index of Realism

The central contribution of AI Cinematic Realism is the concept of emotional plausibility. Unlike photorealism, which measures accuracy against the physical world, emotional plausibility measures coherence against the felt world (Gutierrez, 2026).

An image is emotionally plausible when:

• its affective logic is consistent
• its characters behave believably
• its atmosphere supports its emotional tone
• its world feels experientially true

Emotional plausibility is the new indexicality: the marker of a world that feels real even when it is not photographic.

Human–AI Co-Authorship and Ethical Realism

AI Cinematic Realism foregrounds the collaborative nature of synthetic image-making. The human creator provides:

• intention
• narrative logic
• ethical framing
• aesthetic judgment

The AI provides:

• generative variation
• associative inference
• atmospheric richness
• emergent coherence

Realism arises from the interplay between these contributions. Ethical practice requires acknowledging this interplay rather than obscuring it.

Implications for Scholarship, Creativity, and Institutions

AI Cinematic Realism offers a framework for:

Scholars

• analyzing synthetic images without defaulting to indexical anxiety
• theorizing realism as a cognitive phenomenon
• situating AI within the history of cinematic form

Artists

• designing AI workflows that prioritize coherence over spectacle
• cultivating atmospheric and emotional depth
• embracing co-authorship as a creative method

Institutions

• developing ethical guidelines grounded in realism rather than fear
• teaching responsible generative practices
• evaluating AI images with conceptual rigor

Conclusion

AI Cinematic Realism argues that realism has never belonged to the camera. It belongs to the viewer—to the cognitive, affective, and imaginative processes that make images meaningful. AI does not threaten realism; it reveals its true nature.

As synthetic images proliferate, the task of film theory is not to defend the photographic past but to articulate the cinematic possibilities of the present. AI Cinematic Realism offers one such articulation: a framework for understanding how images without cameras can still feel real, still move us, and still belong to the evolving history of cinema.

References

Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19.

Gutierrez, J. (2026). AI cinematic realism. Independently published.