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 semitransparent hair appearance and to exactly reproduce the interaction of the lights with the scene. We use two constraints to ensure temporal coherence and highlight stability. Our approach outperforms and is computationally more efficient than previous methods.

中文翻译：

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基于GAN的时间稳定着色模型，用于逼真的头发的快速动画制作

我们介绍了一种基于GAN的无监督模型来为逼真的头发动画着色。我们的模型比以前的渲染算法快得多，并且比其他神经图像翻译方法产生的伪像更少。主要思想是扩展Cycle-GAN结构，以避免出现半透明的头发外观，并精确地再现灯光与场景之间的相互作用。我们使用两个约束来确保时间连贯性并突出显示稳定性。与以前的方法相比，我们的方法表现出色，并且在计算效率上更高。