Skinning vector graphics with GANs

Ankit Phogat, Matthew Fisher, Danny M. Kaufman, Vineet Batra

Abstract: 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.