Perceptual-aware Sketch Simplification Based on Integrated VGG Layers.

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ID: 50670
2019
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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.
Reference Key
xu2019perceptualawareieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xu, Xuemiao;Xie, Minshan;Miao, Peiqi;Qu, Wei;Xiao, Wenpeng;Zhang, Huaidong;Liu, Xueting;Wong, Tien-Tsin;
Journal ieee transactions on visualization and computer graphics
Year 2019
DOI
10.1109/TVCG.2019.2930512
URL
Keywords
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