Differentiable vector graphics rasterization for editing and learning
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 27, 2020
- Source ID
- 10.1145/3414685.3417871
Entities
People
- Jonathan Ragan-Kelley
- Michael Gharbi
- Michal Lukáč
- Tzu-mao Li
Organizations
- Adobe
- Defense Advanced Research Projects Agency
- Massachusetts Institute of Technology