PaintCopilot: 将绘画建模为自主的艺术延续
PaintCopilot: Modeling Painting as Autonomous Artistic Continuation
AI总结：
本文提出了一种基于神经网络的绘画助手PaintCopilot，通过建模绘画作为开放性自回归艺术行为，基于不断演变的画布状态和先前笔触历史，无需目标图像即可预测未来笔触，与现有神经绘画方法不同，后者将绘画建模为向预定参考图像的像素重建。
摘要：
我们提出了PaintCopilot，一种协作式神经绘画助手，将绘画建模为一种开放性自回归的艺术行为，该行为基于不断演变的画布状态和先前笔触历史，而无需目标图像。与现有神经绘画方法不同，后者将绘画建模为向预定参考图像的像素重建，PaintCopilot直接从学习到的艺术动态中预测未来的笔触，类似于大型语言模型通过先前上下文继续文本序列。该框架提出了三个互补的模型：基于ViT的目标预测器，通过部分画布观察推断艺术家意图；自回归的下一笔预测器，通过流匹配生成时间上连贯的笔触；以及基于VAE的区域采样器，可按需合成语义本地化的笔触序列。基于三种可微分的笔触表示（硬圆、笔尖和2D高斯），系统支持四种交互工作流程：优化历史、笔触完成、区域修复和动态笔刷。通过与专业艺术家的案例研究，我们证明PaintCopilot能够实现流畅的协作绘画工作流程，在创作过程中艺术家和AI不断交替控制。
英文摘要：
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 conditional VAE, trained on a constructed dataset of 3000 portraits with stroke-level supervision. This lets AI autonomously act on an artwork whose direction is still open, through interruptible, revisable delegation mechanisms spanning individual strokes, extended sequences, bounded regions, and prior stroke history. A two-week deployment with ten artists suggests that creative agency was shaped not simply by how much work artists delegated, but by their ability to continually negotiate the direction, scope, and persistence of AI participation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
