The Script-to-Sequence Pipeline

Traditional storyboarding AI operates frame-by-frame, requiring artists to re-describe context for each panel. This approach ignores the fundamental unit of cinematic storytelling: the scene. A scene carries narrative context — characters, setting, emotional tone, temporal continuity — that should propagate across all its visual representations without requiring re-specification.

We present a document-level approach that ingests complete scripts, maintains narrative context, and generates coherent visual sequences that respect story structure. The pipeline understands screenplay format, identifies scene boundaries, extracts character appearances, and applies appropriate visual treatment based on narrative context — transforming the script from a prompt source into a creative intelligence layer.

Current AI generation tools treat each image as an independent prompt-response pair. When creating a storyboard sequence, the creator must:

• Re-establish context: Every panel requires re-describing the scene setting, time of day, weather, and environment.
• Re-describe characters: Physical descriptions must be restated or referenced for each shot to maintain continuity.
• Maintain emotional progression: The narrative arc within a scene must be manually managed across individual generations.
• Track visual continuity: Lighting, color palette, and compositional language must be manually consistent.

This per-frame cognitive overhead is precisely the "extraneous cognitive load" that Nielsen Norman Group (2013) identifies as the primary barrier to productive tool usage.

Our pipeline processes the complete screenplay as a structured document, applying the screenplay structure theory established by Field (1979) to decompose the narrative into actionable units:

• Scene boundary detection: Automatic identification of scene headers (INT./EXT., location, time of day) to establish contextual boundaries.
• Character tracking: Character appearances, entrances, and exits are tracked across the document, enabling consistent characters without re-specifying them each shot.
• Emotional arc mapping: Action lines and dialogue are analyzed for emotional trajectory, informing lighting, composition, and color treatment choices.
• Coverage suggestion: Based on scene type (dialogue, action, transition), the system suggests appropriate shot coverage following established cinematographic conventions (Katz, 1991).

The Script workspace in AIM Previz implements this pipeline. Creators can paste or generate a script in industry-standard format, and the system automatically breaks it down into scenes with appropriate visual parameters. Each scene inherits its context — characters, location, time, mood — without requiring the creator to re-specify any contextual information. The output flows directly into the Storyboard and Keyframe workspaces, maintaining narrative coherence across the entire production pipeline.

The script is the source of truth for any visual production. AI generation systems that ignore document-level context force creators to become manual context managers — repeatedly re-stating information that the script already contains. By treating the screenplay as an intelligence layer rather than a prompt source, we eliminate this redundant cognitive burden and produce visual sequences that respect the narrative structure of professional production.