From Reference-Based To Intent-Driven Color: Toward Fully Automated Sequence Color... Dado Valentic

From Reference-Based To Intent-Driven Color: Toward Fully Automated Sequence Color Matching - Dado Valentic, Color Intelligence Color matching has traditionally relied on user-selected references and manual intervention, even when assisted by machine learning. While recent AI-based systems have accelerated matching and balancing, they still depend on human reference selection and subjective interpretation of creative intent. This presentation introduces a shift from reference-driven workflows to intent-driven automation. By combining multimodal neural networks, image and sequence content analysis, and natural-language descriptors, the system can automatically determine optimal references and apply consistent color matching across entire sequences—without explicit reference selection by the user. The talk explores how visual semantics, scene context, and descriptive intent can be modeled to enable automated sequence-level color matching. It discusses architectural considerations, limitations, and integration challenges in modern studio pipelines, with a focus on scalability and maintaining artist oversight. Rather than presenting a finished solution, the session aims to open discussion around automation boundaries and the role of multimodal AI in future color and imaging pipelines.