Difference between revisions of "Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup"
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Sketch Simplification is research paper relating to artificial intelligence and drawing.
Contents
Description
From the introduction:
We present a novel technique to simplify sketch drawings based on learning a series of convolution operators.
In contrast to existing approaches that require vector images as input, we allow the more general and challenging input of rough raster sketches such as those obtained from scanning pencil sketches.
We convert the rough sketch into a simplified version which is then amendable for vectorization. This is all done in a fully automatic way without user intervention.
Our model consists of a fully convolutional neural network which, unlike most existing convolutional neural networks, is able to process images of any dimensions and aspect ratio as input, and outputs a simplified sketch which has the same dimensions as the input image.
In order to teach our model to simplify, we present a new dataset of pairs of rough and simplified sketch drawings.
By leveraging convolution operators in combination with efficient use of our proposed dataset, we are able to train our sketch simplification model.
Authors
Edgar Simo-Serra, Satoshi Iizuka, Kazuma Sasaki, and Hiroshi Ishikawa
