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Research Article | Open Access

A GAN-based temporally stable shading model for fast animation of photorealistic hair

Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
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Abstract

We introduce an unsupervised GAN-based model for shading photorealistic hair animations. Our model is much faster than previous rendering algorithms and produces fewer artifacts than other neural image translation methods. The main idea is to extend the Cycle-GAN structure to avoid semi-transparent hair appearance and to exactly reproduce the interaction of the lights with the scene. We usetwo constraints to ensure temporal coherence and highlight stability. Our approach outperforms and is computationally more efficient than previous methods.

Keywords

neural networkshair animationfast shading

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41095_2020_201_MOESM1_ESM.mp4

References

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Computational Visual Media
Volume 7 Issue 1,
March 2021
Pages 127-138
DOI: 10.1007/s41095-020-0201-9
Cite this article:
Qiao Z, Kanai T. A GAN-based temporally stable shading model for fast animation of photorealistic hair. Computational Visual Media, 2021, 7(1): 127-138. https://doi.org/10.1007/s41095-020-0201-9

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Received: 20 September 2020
Accepted: 27 November 2020
Published: 18 January 2021
© The Author(s) 2020

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