English(EN) PaintCopilot: Modeling Painting as Autonomous Artistic Continuation

AI系统PaintCopilot模拟自主艺术绘画延续

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了PaintCopilot，一个新颖的AI系统，旨在通过预测合理的绘画动作来辅助艺术绘画，而无需预定义的最终图像。与现有的目标驱动方法不同，PaintCopilot使用三个专用模型：一个目标预测器（Target Predictor）从不断变化的画布中推断视觉目标，一个笔触预测器（Stroke Predictor）通过流匹配生成笔触序列，以及一个区域采样器（Region Sampler）使用条件VAE在指定区域内生成笔触。该系统在具有笔触级别监督的3000幅肖像数据集上进行训练，允许在从单个笔触到历史上下文的各种级别上进行可中断和可修改的AI参与。 AI

影响这项研究为AI在创意领域开辟了新范式，超越了重建，实现了自主的艺术贡献。

排序理由研究论文，详细介绍了一个新的艺术绘画AI模型。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Yunge Wen, Yaluo Wang, Yuancheng Shen, Robert Krueger, Paul Pu Liang · 2026-09-15 04:00

PaintCopilot：将绘画建模为自主艺术性延续

arXiv:2605.20941v2 Announce Type: replace Abstract: 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 actio…

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PaintCopilot：将绘画建模为自主艺术性延续

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