Parametric fur from an image
Parametric fur from an image
复制标题
图像中的参数化毛发
DOI：
10.1007/s00371-020-01857-x
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Igarashi Takeo
中科院分区：
文献类型：
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作者：
Noh Seung-Tak;Takahashi Kenichi;Adachi Masahiko;Igarashi Takeo
参数化毛发是计算机图形学内容创建的强大工具。然而，设置参数以实现期望的结果是困难的。为了解决这个问题，我们提出了一种从图像中自动估计适当参数的方法。我们将该过程表述为一个优化问题，其中系统搜索参数，以使渲染的参数化毛发的外观尽可能与真实毛发的外观相似。在每个优化步骤中，我们使用现成的毛皮渲染器渲染图像，并使用预先训练的深度卷积神经网络模型测量图像相似度。我们证明所提出的方法可以适当地估计各种毛发类型的毛发参数。
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.