We present PaintCopilot, a co-creative neural painting assistant that models painting as an open-ended autoregressive artistic behavior conditioned on evolving canvas states and prior brushstroke history, without requiring a target image. Unlike existing neural painting methods that frame painting as pixel reconstruction toward a predefined reference, PaintCopilot predicts future strokes directly from learned artistic dynamics, analogous to how large language models continue text sequences from prior context. The framework proposes three complementary models: a ViT-based Target Predictor that infers artist intent from partial canvas observations, an autoregressive Next Stroke Predictor that generates temporally coherent brushstrokes via flow matching, and a VAE-based Region Sampler that synthesizes semantically localized stroke sequences on demand. Built on three differentiable brush representations (Hard Round, Brush Tip, and 2D Gaussian), the system supports four interactive workflows: Optimize History, Stroke Completion, Region Inpainting, and Dynamic Brush. Through case studies with professional artists, we demonstrate that PaintCopilot enables fluid co-creative painting workflows in which artists and AI continuously alternate control throughout the creative process.

翻译：我们提出PaintCopilot，一种协作式神经绘画助手，将绘画建模为基于演化画布状态和先前笔触历史的无约束自回归艺术行为，无需依赖目标图像。与现有将绘画视为面向预定义参考图像的像素重建的神经绘画方法不同，PaintCopilot直接从习得的艺术动力学预测未来笔触，类似于大语言模型基于先前上下文续写文本序列的方式。该框架提出三个互补模型：基于ViT的目标预测器（从部分画布观测推断艺术家意图）、通过流匹配生成时序连贯笔触的自回归下一笔触预测器，以及按需合成语义局部笔触序列的基于VAE的区域采样器。系统基于三种可微笔刷表征（硬圆笔刷、笔刷尖端与二维高斯分布），支持四种交互工作流：历史优化、笔触补全、区域修复与动态笔刷。通过与专业艺术家的案例研究，我们证明PaintCopilot能够实现艺术家与AI在创作过程中持续交替控制的流畅协作式绘画工作流。