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• W2912552022 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." @default.
• W2912552022 created "2019-02-21" @default.
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• W2912552022 date "2019-01-01" @default.
• W2912552022 modified "2023-09-30" @default.
• W2912552022 title "Deep Normal Estimation for Automatic Shading of Hand-Drawn Characters" @default.
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• W2912552022 doi "https://doi.org/10.1007/978-3-030-11015-4_20" @default.
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