Graphics Programming weekly - Issue 372 - December 29, 2024


December 13th, 2024 PIX 2412.12 – Texture/Buffer/Visualizer improvements, Work Graph Shader Debugging, and many Timing Capture improvements

  • the blog post discusses changes in the latest release of Pix
  • adds support for VRS shading rate visualization, shader graph debugging, as well as improved custom shader support


UNORM and SNORM to float, hardware edition

  • the article provides a detailed discussion of how hardware implements the UNORM/SNORM conversion rules
  • explaining special cases and how to deal with those
  • additionally, it presents a C++ implementation of the presented concepts


Efficient Motion Blurred Spheres Using Texture Mapping

  • the paper presents a model of applying motion blur to spheres in a 2.5D setting
  • implemented by pre-generating an atlas of blurred spheres of varying radius and lighting angles and compositing these into the scene
  • explains the implementation and limitations
  • runnable example and source code is provided


Moana 2

  • the blog post discusses changes done to the Disney Hyperion render since the prequel to the movie
  • provides published research papers on the discussed techniques
  • then presents the main challenges of the Moana 2 production and how they solved them


Transforming normals: adjugate transpose vs inverse transpose

  • the blog post discusses the difference between adjugate transpose vs inverse transpose of normals
  • explains how it behaves on shading normals
  • presents the visual results of different methods


[video] Game Engine Programming 074.1 - Brief introduction to importance sampling

  • the video provides an introduction into the mathematics of importance sampling
  • discusses how samples can approximate integrals
  • then applies the ideas to the diffuse shading integral


Noise, Neural Networks, and Flow-Matching

  • the article explains and shows how noise applied to a curve fitting application changes the results
  • additionally presents the same in a multi-modality model using flow matching
  • concluding that “noise cancels out, signal adds up” cannot always be relied upon anymore


Visualize and understand GPU memory in PyTorch

  • the blog post presents tools available to visualize and understand memory usage in PyTorch
  • explains how to estimate memory usage before running a workload
  • additionally provides a small tool to help with the estimation


Thanks to Nia Bickford for support of this series.


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