Differentiable Vector Graphics Rasterization for Editing and Learning
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 differenti...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|SIGGRAPH Asia 2020 Technical Papers
2025
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| Online Access: | https://hdl.handle.net/1721.1/158158 |