Learning to Trace: Expressive Line Drawing Generation from Photographs. (14th November 2019)

• Record Type: Journal Article
• Authors: Inoue, N.; Ito, D.; Xu, N.; Yang, J.; Price, B.; Yamasaki, T.
• Is Part Of: Computer graphics forum. Volume 38:Number 7(2019)
• Page Start: 69
• Page End: 80
• Publication Date: 2019-11-14
• Subjects: CCS Concepts -- Computing methodologies → Image manipulation -- Applied computing → Fine arts; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13817
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: In this paper, we present a new computational method for automatically tracing high‐resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large‐scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end‐to‐end trainable fully‐convolutional CNN to learn the model in a data‐driven manner. The model consists of two networks to cope with two sub‐tasks; extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications.