Perceptual-aware sketch simplification based on integrated VGG layers
Author(s)
Author(s)
Xu, X.
Xie, M.
Miao, P.
Qu, W.
Xiao, W.
Zhang, H.
Wong, T.-T.
Date Issued
2021
Publisher
IEEE
Journal
IEEE Transactions on Visualization and Computer Graphics
Volume
27
Issue
1
Start page
178
End page
189
Abstract
Deep learning has been recently demonstrated as an effective tool for raster-based sketch simplification. Nevertheless, it remains challenging to simplify extremely rough sketches. We found that a simplification network trained with a simple loss, such as pixel loss or discriminator loss, may fail to retain the semantically meaningful details when simplifying a very sketchy and complicated drawing. In this paper, we show that, with a well-designed multi-layer perceptual loss, we are able to obtain aesthetic and neat simplification results preserving semantically important global structures as well as fine details without blurriness and excessive emphasis on local structures. To do so, we design a multi-layer discriminator by fusing all VGG feature layers to differentiate sketches and clean lines. The weights used in layer fusing are automatically learned via an intelligent adjustment mechanism. Furthermore, to evaluate our method, we compare our method to state-of-the-art methods through multiple experiments, including visual comparison and intensive user study.
SFU Affiliated Publication
Yes
Availability at SFU Library
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