{"type":"ScholarlyArticle","title":"Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning","authors":[{"name":"Ning Xie","slug":"ning-xie","given":"Ning","family":"Xie","affiliation":""},{"name":"Tingting Zhao","slug":"tingting-zhao","given":"Tingting","family":"Zhao","affiliation":""},{"name":"Feng Tian","slug":"feng-tian","given":"Feng","family":"Tian","affiliation":""},{"name":"Xiaohua Zhang","slug":"xiaohua-zhang","given":"Xiaohua","family":"Zhang","affiliation":""},{"name":"Masashi Sugiyama","slug":"masashi-sugiyama","given":"Masashi","family":"Sugiyama","affiliation":""}],"abstract":"Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, we develop an AI-aided art authoring (A4) system of non-photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist's style. Within the reinforcement learning framework of brush stroke generation proposed, our contribution in this paper is to learn artists' drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.","doi":null,"url":"https://ijcai.org/proceedings/2015/359","pdf":"https://ijcai.org/proceedings/2015/0359.pdf","markdown":"https://ijcai.org/proceedings/2015/359.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/359","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Artificial Intelligence and the Arts","track":"Special Track on Artificial Intelligence and the Arts","number":359,"pages":{"first":2531,"last":2538},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}