From Image Parsing to Painterly Rendering

Kun Zeng1,*     Mingtian Zhao1,2,*     Caiming Xiong1     Song-Chun Zhu1,2
1Lotus Hill Institute       2University of California, Los Angeles
(*equally contributed authors)


We present a semantics-driven approach for stroke-based painterly rendering, based on recent image parsing techniques [Tu et al. 2005; Tu and Zhu 2006] in computer vision. Image parsing integrates segmentation for regions, sketching for curves, and recognition for object categories. In an interactive manner, we decompose an input image into a hierarchy of its constituent components in a parse tree representation with occlusion relations among the nodes in the tree. To paint the image, we build a brush dictionary containing a large set (760) of brush examples of four shape/appearance categories, which are collected from professional artists, then we select appropriate brushes from the dictionary and place them on the canvas guided by the image semantics included in the parse tree, with each image component and layer painted in various styles. During this process, the scene and object categories also determine the color blending and shading strategies for inhomogeneous synthesis of image details. Compared with previous methods, this approach benefits from richer meaningful image semantic information, which leads to better simulation of painting techniques of artists using the high-quality brush dictionary. We have tested our approach on a large number (hundreds) of images and it produced satisfactory painterly effects.

Paper and Slides

Paper Slides presented at SIGGRAPH 2010


  author = {Kun Zeng and Mingtian Zhao and Caiming Xiong and Song-Chun Zhu},
  title = {From image parsing to painterly rendering},
  journal = {ACM Trans. Graph.},
  volume = {29},
  number = {1},
  year = {2009},
  issn = {0730-0301},
  pages = {2:1--2:11},
  doi = {},
  publisher = {ACM},
  address = {New York, NY, USA},



  • This article was presented at SIGGRAPH 2010, Los Angeles, California.
  • The teaser painting image was adopted as the cover picture of this article's TOG issue.
  • Correspondence and inquiries about this article should be directed to M. Zhao (mtzhao (at) or S.-C. Zhu (sczhu (at)

Related Publications

Painterly Animation Using Video Semantics and Feature Correspondence
Liang Lin, Kun Zeng, Han Lv, Yizhou Wang, Ying-Qing Xu, and Song-Chun Zhu
International Symposium on Non-Photorealistic Animation and Rendering (NPAR) 2010, Annecy, France.

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