Learning to Trace: Expressive Line Drawing Generation from Photographs
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
@article{10.1111:cgf.13817,
journal = {Computer Graphics Forum},
title = {{Learning to Trace: Expressive Line Drawing Generation from Photographs}},
author = {Inoue, Naoto and Ito, Daichi and Xu, Ning and Yang, Jimei and Price, Brian and Yamasaki, Toshihiko},
year = {2019},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13817}
}