3 accepted papers
The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations such as vector graphics. Unfortunately due to their variable structure and scarcity of vector training data directly applying…
Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic designs. But, while a vast body of work has focused on generative algorithms for raster images, only a handful of options exists for vector graphics. One can always rasterize the input graphic and resort to imag…
This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a recons…