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Differentiable vector graphics rasterization for editing and learning

Authors:

Michal Lukáč,

Michaël Gharbi,

Jonathan Ragan-KelleyAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 39, Issue 6

Article No.: 193, Pages 1 - 15

https://doi.org/10.1145/3414685.3417871

Published: 27 November 2020 Publication History

Abstract

We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content. We observe that vector graphics rasterization is differentiable after pixel prefiltering. Our differentiable rasterizer offers two prefiltering options: an analytical prefiltering technique and a multisampling anti-aliasing technique. The analytical variant is faster but can suffer from artifacts such as conflation. The multisampling variant is still efficient, and can render high-quality images while computing unbiased gradients for each pixel with respect to curve parameters.

We demonstrate that our rasterizer enables new applications, including a vector graphics editor guided by image metrics, a painterly rendering algorithm that fits vector primitives to an image by minimizing a deep perceptual loss function, new vector graphics editing algorithms that exploit well-known image processing methods such as seam carving, and deep generative models that generate vector content from raster-only supervision under a VAE or GAN training objective.

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Index Terms

Differentiable vector graphics rasterization for editing and learning
1. Computing methodologies
  1. Computer graphics

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 39, Issue 6

December 2020

1605 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3414685

Editor:
Karol Myszkowski
MPI Informatik

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Published: 27 November 2020

Published in TOG Volume 39, Issue 6

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