Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters

Matis Hudon, Mairead Grogan, Rafael Pages, Aljosa Smolic; The European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Abstract


We present a new fully automatic pipeline for generating shading effects on hand-drawn characters. Our method takes as input a single digitized sketch of any resolution and outputs a dense normal map estimation suitable for rendering without requiring any human input. At the heart of our method lies a deep residual, encoder-decoder convolutional network. The input sketch is first sampled using several equally sized 3-channel windows, with each window capturing a local area of interest at 3 different scales. Each window is then passed through the previously trained network for normal estimation. Finally, network outputs are arranged together to form a full-size normal map of the input sketch. We also present an efficient and effective way to generate a rich set of training data. Resulting renders offer a rich quality without any effort from the 2D artist. We show both quantitative and qualitative results demonstrating the effectiveness and quality of our network and method.

Related Material


[pdf]
[bibtex]
@InProceedings{Hudon_2018_ECCV_Workshops,
author = {Hudon, Matis and Grogan, Mairead and Pages, Rafael and Smolic, Aljosa},
title = {Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters},
booktitle = {The European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}