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Learning to Trace: Expressive Line Drawing Generation from Photographs
Author(s) -
Inoue N.,
Ito D.,
Xu N.,
Yang J.,
Price B.,
Yamasaki T.
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13817
Subject(s) - computer science , tracing , trace (psycholinguistics) , line drawings , line (geometry) , convolutional neural network , construct (python library) , artificial intelligence , face (sociological concept) , computer graphics (images) , computer vision , engineering drawing , programming language , social science , philosophy , linguistics , geometry , mathematics , sociology , engineering
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.