中文题目:机器人绘画的艺术风格:一种从人类艺术家那里学习笔触的机器学习方法
中文摘要:自20世纪70年代以来,机器人绘画一直是艺术家和机器人专家们感兴趣的课题。研究人员和跨学科艺术家们利用各种绘画技术和人机协作模型在画布上创建视觉媒介。机器人绘画的挑战之一是如何将理想的艺术风格应用到绘画中。风格转换技术与机器学习模型已经帮助我们解决这一挑战与视觉风格的具体绘画。然而,其他的手工风格元素,即画家的绘画技巧和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,将艺术风格融入到笔触和绘画过程中。在本文中,我们描述了我们的方法:1)收集艺术家的笔触和手笔运动样本;2)训练生成模型,生成符合艺术家风格的笔触;3)将学习到的模型整合到机器人手臂上,在画布上作画。在一项初步研究中,71%的人类评估者认为我们的机器人画与艺术家的风格特征有关。
英文题目:Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists
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) integrate the learned model on a robot arm to paint on a canvas. In a preliminary study, 71% of human evaluators find our robot's paintings pertaining to the characteristics of the artist's style.
原文作者:Ardavan Bidgoli, Manuel Ladron De Guevara, Cinnie Hsiung, Jean Oh, Eunsu Kang
原文地址:https://arxiv.org/abs/2007.03647
PDF链接:https://arxiv.org/ftp/arxiv/papers/2007/2007.03647.pdf
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