Brushes in Motion: Vector Guided Strokes for Computational Painting

Understanding and computationally modeling the creative process behind artistic creation remains a fundamental challenge in Creative AI. While existing neural painting methods focus on replicating final visual outcomes, they largely ignore the sequential, hierarchical decision-making that characterizes human artistic workflow - the dynamic motion of brushes across canvas that brings art to life. We introduce a novel approach to computationally decompose and reconstruct the painting process itself, revealing how artworks emerge through systematic region-aware brushstroke sequences that mirror a more natural artistic practice. Our method leverages image vectorization to extract semantic painting regions and develops algorithms to estimate brushstroke parameters and sequencing strategies that progress from global compositional structure to localized detail refinement. This enables generation of stroke-by-stroke painting animations that expose the underlying creative process in motion, with applications ranging from immersive museum experiences to collaborative AI-assisted art creation platforms.