We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.

中文翻译：

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用于高性能可微渲染的模块化基元

我们提出了一种模块化可微渲染器设计，通过利用现有的、高度优化的硬件图形管道，其性能优于以前的方法。我们的设计支持现代图形管道中的所有关键操作：光栅化大量三角形、属性插值、过滤纹理查找以及用户可编程着色和几何处理，所有这些都以高分辨率进行。我们的模块化原语允许在 PyTorch 或 TensorFlow 等自动微分框架中直接构建自定义的高性能图形管道。作为一个激励应用程序，我们将面部表现捕捉制定为一个逆渲染问题，并表明可以使用我们的工具有效地解决它。