SRNPD: Spatial rendering network for pencil drawing stylization

Yuxi Jin, Ping Li, Bin Sheng, Yongwei Nie, Jinman Kim, Enhua Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Pencil drawing is a simple yet effective way to depict what people see by clearly presenting details of the scene. Existing methods usually extract strokes of the input image and adjust the result image tone to make it look like a pencil drawing. However, they do not consider the quality of the stroke image and the geometry information of lines in the stroke image, which unavoidably results in the violation of original essential structures and in a flatten pencil drawing with unrealistic appearance. We put forward a spatial rendering network for pencil drawing stylization. Spatial stroke images are extracted from the image pyramid by a single-shot bottom-up neural network to improve the quality of these stroke images. Unlike the former tone adjustment–based methods, we analyze perceptual cues of strokes at different stroke image levels and use the obtained geometry information to constrain the stroke shading procedure. The final pencil drawing result is achieved by the stroke shading fusion of different levels' shading results. The effectiveness of our spatial rendering network for pencil drawing stylization is demonstrated by an ablation study, comparison to the state of the art, and a user study.

Original languageEnglish
Article numbere1890
JournalComputer Animation and Virtual Worlds
Volume30
Issue number3-4
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • geometry information constraint
  • single-shot bottom-up
  • spatial rendering network
  • stroke shading fusion

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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