PaintCopilot: Modeling Painting as Autonomous Artistic Continuation
Existing neural painting methods are target-driven: given a reference image, strokes are optimized to reconstruct it, fixing the outcome before painting begins. We instead ask whether a model can predict plausible painting actions when the final image is unknown, a problem we call autonomous artistic continuation. PaintCopilot realizes this through three lightweight, task-specific models: a Target Predictor that infers a provisional visual target directly from the evolving canvas, a Stroke Predictor that formulates brushstroke generation as flow matching to capture multiple plausible continuations, and a Region Sampler that generates stroke sequences within artist-specified regions via a ...
| Authors | Yunge Wen, Yaluo Wang, Yuancheng Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 May 2026 |
| Last updated at source | 13 September 2026 |
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