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http://dx.doi.org/10.9708/jksci.2022.27.05.085

Hair and Fur Synthesizer via ConvNet Using Strand Geometry Images  

Kim, Jong-Hyun (School of Software Application, Kangnam University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.5, 2022 , pp. 85-92 More about this Journal
Abstract
In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.
Keywords
Hair simulation; Fur simulation; Convolutional neural network; Physically based simulation; Strand geometry image;
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Times Cited By KSCI : 3  (Citation Analysis)
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