皮肤矢量图形与gan

Ankit Phogat, Matthew Fisher, D. Kaufman, Vineet Batra
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引用次数: 1

摘要

我们提出了一种新的编辑矢量图形的方法,使用户能够直观地修改复杂的bsamzier几何。我们的方法使用生成对抗网络(GAN)来自动预测由三次bsamizier曲线定义的任意几何形状的突出点,这些突出点用作线性混合蒙皮变换的处理位置。此外,我们将输入几何图形绑定到三角形网格,以将输入几何图形的复杂性与网格拓扑解耦。最后,为了从变换后的网格中重构bsamzier曲线,我们在不增加bsamzier段数量的前提下,制定了一个线性优化问题,并以高性能的方式进行求解,以保证实时反馈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skinning vector graphics with GANs
We propose a novel method for editing vector graphics which enables users to intuitively modify complex Bézier geometry. Our method uses a Generative Adversarial Network (GAN) to automatically predict salient points for any arbitrary geometry defined by cubic Bézier curves, which are used as handle locations for a Linear Blend Skinning transformation. Further, we bind input geometry to a triangle mesh, to decouple the complexity of input geometry from mesh topology. Finally, to reconstruct Bézier curves from the transformed mesh, we formulate a linear optimization problem and solve it in performant manner to ensure real time feedback, without increasing the number of Bézier segments.
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