Lecture 26

Learning

Thoughts

  • Covered a variety of learning applications in simulation & animation
  • I still think the most compelling uses are seen in character animation, as mo-cap data sets provide rich basic data that can be intelligently interpolated & mixed by learning algorithms.
  • Still, learning for up-scaling simulations makes a lot of sense. Currently we’re seeing the wide adoption of deep up-sampling for images (NVIDIA DLSS), which is really just up-scaling a light transport simulation. So, it makes total sense that we could also apply these techniques to fluid/smoke or other physics simulations. I wonder if it is viable to train an entire physics and rendering system end-to-end, now that differentiable path tracing is also reasonable.
Written on May 5, 2021