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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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.
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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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DOI: https://doi.org/10.1007/s00371-020-01857-x