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Parametric fur from an image

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Abstract

Parametric fur is a powerful tool for content creation in computer graphics. However, setting parameters to realize the desired result is difficult. To address this problem, we propose a method to automatically estimate appropriate parameters from an image. We formulate the process as an optimization problem wherein the system searches for parameters such that the appearance of the rendered parametric fur is as similar as possible to the appearance of the real fur. In each optimization step, we render an image using an off-the-shelf fur renderer and measure image similarity using a pre-trained deep convolutional neural network model. We demonstrate that the proposed method can estimate fur parameters appropriately for a wide range of fur types.

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Acknowledgements

We would like to thank Zheyuan Cai for helping on the preliminary experiment.

Funding

This work was partially supported by JSPS KAKENHI (Grant Number JP17H00752 and 19J13492). Seung-Tak Noh is funded by JSPS Research Fellowships for Young Scientists.

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Authors and Affiliations

  1. The University of Tokyo, Bunkyo, Japan

    Seung-Tak Noh & Takeo Igarashi

  2. Kabuku Inc., Shinjuku, Japan

    Kenichi Takahashi & Masahiko Adachi

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  1. Seung-Tak Noh
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  2. Kenichi Takahashi
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  3. Masahiko Adachi
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  4. Takeo Igarashi
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Correspondence to Seung-Tak Noh.

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Fur parameters

Fur parameters

The selected fur parameters are listed in Table 1. We selected 25 parameters from 89 parameters in Maya Fur and converted them to normalized space. In our experiment, we fixed the other 64 parameters as default values.

Table 1 The selected 25 parameters in our framework. Among them, 15 parameters are related to the fur geometry and 10 are related to color
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Noh, ST., Takahashi, K., Adachi, M. et al. Parametric fur from an image. Vis Comput 37, 1129–1138 (2021). https://doi.org/10.1007/s00371-020-01857-x

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