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.

中文翻译：

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用于编辑和学习的可微矢量图形光栅化

我们引入了一种可微分光栅化器，它连接矢量图形和光栅图像域，支持强大的基于光栅的损失函数、优化过程和机器学习技术来编辑和生成矢量内容。我们观察到矢量图形光栅化在像素预过滤后是可微的。我们的可微分光栅器提供两种预过滤选项：分析预过滤技术和多重采样抗锯齿技术。分析变体速度更快，但可能会受到混淆等伪影的影响。多重采样变体仍然有效，并且可以渲染高质量图像，同时计算每个像素相对于曲线参数的无偏梯度。我们证明我们的光栅化器支持新的应用程序，包括由图像指标引导的矢量图形编辑器，