机器人绘画中的艺术风格;一种从人类艺术家那里学习笔触的机器学习方法
Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists
摘要：
自20世纪70年代以来，机器人绘画一直是艺术家和机器人学家感兴趣的主题。研究人员和跨学科艺术家采用各种绘画技术和人机协作模型在画布上创造视觉媒介。机器人绘画的挑战之一是将所需的艺术风格应用于绘画。使用机器学习模型的风格转换技术帮助我们通过特定绘画的视觉风格来解决这一挑战。然而，其他风格的手工元素，即，绘画技巧和一个艺术家的笔触，还没有得到充分的解决。我们提出了一种方法，通过与人类艺术家的合作，将艺术风格整合到笔触和绘画过程中。在本文中，我们描述了我们的方法，1）从艺术家那里收集笔触和手刷运动样本，2）训练生成模型来生成与艺术家风格相关的笔触，3）微调基于笔触的渲染模型，以与我们的机器人绘画设置一起工作。我们将在单独的出版物中报告这三个步骤的整合。在一项初步研究中，71%的人类评估者发现我们重建的笔触与艺术家的风格特征有关。此外，58%的参与者无法区分用我们的方法制作的绘画与人类艺术家创作的视觉相似的绘画。
英文摘要：
Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniques and human-robot collaboration models to create visual mediums on canvas. One of the challenges of robotic painting is to apply a desired artistic style to the painting. Style transfer techniques with machine learning models have helped us address this challenge with the visual style of a specific painting. However, other manual elements of style, i.e., painting techniques and brushstrokes of an artist, have not been fully addressed. We propose a method to integrate an artistic style to the brushstrokes and the painting process through collaboration with a human artist. In this paper, we describe our approach to 1) collect brushstrokes and hand-brush motion samples from an artist, and 2) train a generative model to generate brushstrokes that pertains to the artist's style, and 3) fine tune a stroke-based rendering model to work with our robotic painting setup. We will report on the integration of these three steps in a separate publication. In a preliminary study, 71% of human evaluators find our reconstructed brushstrokes are pertaining to the characteristics of the artist's style. Moreover, 58% of participants could not distinguish a painting made by our method from a visually similar painting created by a human artist.
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。
