PaintCopilot: Modeling Painting as Autonomous Artistic Continuation

Existing neural painting systems primarily formulate painting as reconstructing a predefined target image, limiting their ability to support open-ended artistic creation. We present PaintCopilot, a co-creative painting system that models painting as autonomous artistic continuation conditioned on the current canvas state and prior brushstroke history. The system combines three computational models to estimate artistic intent, predict future brushstrokes, and synthesize localized painting content, enabling four interactive workflows that allow artists to seamlessly alternate between manual painting and AI-assisted creation. We further introduce a dataset of 3,000 portrait paintings paired with simulated painting processes for training autoregressive painting models. Quantitative experiments and an expert case study demonstrate that PaintCopilot supports fluid collaborative painting while preserving artistic flexibility. Our work highlights a new direction for creative AI systems that assist evolving artistic processes rather than generating static images.