Temporal Coherence in Multi-Shot AI Sequences

Multi-shot sequences generated by independent AI models exhibit systematic visual discontinuities — lighting drift, chromatic inconsistency, and character feature migration — that violate the perceptual contracts audiences have developed over a century of edited media. We formalize the problem of temporal coherence across generative boundaries, propose a taxonomy of inter-shot discontinuity types, and present a constraint-propagation framework that anchors visual identity parameters across independently generated clips.

Our approach treats each shot not as an isolated generation but as a node in a connected sequence; the look established early stays consistent in later shots without manual re-matching.

Through systematic analysis of AI-generated multi-shot sequences, we identify four categories of visual discontinuity:

• Lighting drift: Intensity, direction, and color temperature of light sources shift between shots, creating impossible lighting continuity that trained viewers immediately detect.
• Chromatic inconsistency: Color palettes and white balance migrate between shots, producing sequences where the same scene appears to have been graded by different colorists.
• Character feature migration: Facial proportions, hair color, clothing details, and body proportions shift between shots — the identity drift problem addressed by our Continuity Lock research.
• Environment instability: Background elements, set dressing, and spatial relationships change between shots of the same scene.

Rather than treating each generation as independent, we model the sequence as a connected sequence, so the established look carries forward across shots:

• Color histogram anchoring: The first shot establishes the color distribution; subsequent shots are constrained to match.
• Lighting vector propagation: Light direction, intensity, and quality are parameterized from the establishing shot and carried forward through coverage.
• Spatial proportion locking: The relative sizes and positions of key elements are anchored to prevent impossible scale shifts between angles.
• Texture signature persistence: Surface qualities and material properties are encoded and maintained across all shots in a scene.

AIM Previz implements temporal coherence through its deterministic generation architecture. When a creator establishes look parameters — camera profile, lens, lighting mood, film stock, art style — these become project-level constraints that persist across all generations. Combined with Continuity Lock for character identity, this creates a multi-layered coherence system: style coherence (look parameters), identity coherence (character lock), and environmental coherence (spatial constraints).

Temporal coherence across generative boundaries is not an aesthetic luxury — it is the minimum requirement for AI-generated sequences to function as professional pre-visualization. By formalizing discontinuity types and implementing constraint-propagation across the generation pipeline, we bridge the gap between independent frame generation and the coherent visual storytelling that production demands.