用可微分样条建模笔触，让机器人画出更像人类的绘画风格。
Spline-FRIDA: Towards Diverse, Humanlike Robot Painting Styles with a Sample-Efficient, Differentiable Brush Stroke Model
- 用运动捕捉采集真人作画轨迹，通过自编码器建模复杂笔触。
- 相比传统贝塞尔曲线，生成笔触更接近人类风格，提升艺术性。
- 适用于希望实现自然绘画风格的机器人创作与艺术研究者。
一幅画不仅是墙上的图像，更是由多个有意图的笔触构成的过程，笔触形状是绘画风格和表达意义的重要组成部分。以往建模笔触轨迹的方法要么无法应用于真实机器人，要么缺乏灵活性，难以捕捉人类笔触的复杂性。本文提出Spline-FRIDA，通过运动捕捉记录艺术家作画轨迹，利用自编码器对提取的轨迹进行建模，并引入一种新颖的可微分笔触动力学模型，集成至现有机器人绘画平台FRIDA。我们开展调查发现，开源的Spline-FRIDA能有效捕捉人类作画风格；与使用受限贝塞尔曲线的现有机器人绘画系统相比，其生成的笔触更具人类似似性、改善语义规划，并展现出更强的艺术表现力。
原文摘要 · Abstract (English)
A painting is more than just a picture on a wall; a painting is a process comprised of many intentional brush strokes, the shapes of which are an important component of a painting's overall style and message. Prior work in modeling brush stroke trajectories either does not work with real-world robotics or is not flexible enough to capture the complexity of human-made brush strokes. In this work, we introduce Spline-FRIDA which can model complex human brush stroke trajectories. This is achieved by recording artists drawing using motion capture, modeling the extracted trajectories with an autoencoder, and introducing a novel brush stroke dynamics model to the existing robotic painting platform FRIDA. We conducted a survey and found that our open-source Spline-FRIDA approach successfully captures the stroke styles in human drawings and that Spline-FRIDA's brush strokes are more human-like, improve semantic planning, and are more artistic compared to existing robot painting systems with restrictive Bézier curve strokes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。