“Machine-learning Denoising in Feature Film Production” by Dahlberg, Adler and Newlin
Conference:
Type:
Entry Number: 21
Title:
- Machine-learning Denoising in Feature Film Production
Presenter(s)/Author(s):
Abstract:
We present our experience deploying and using machine learning denoising of Monte Carlo renders in the production of animated feature films such as Pixar’s Toy Story 4, Disney Animation’s Ralph Breaks the Internet and Industrial Light & Magic’s visual effects work on photo-realistic films such as Aladdin (2019). We show what it took to move from an R&D implementation of “Denoising with Kernel Prediction and Asymmetric Loss Functions” [Vogels et al. 2018] to a practical tool in a production pipeline.
References:
Brent Burley, David Adler, Matt Jen-Yuan Chiang, Hank Driskill, Ralf Habel, Patrick Kelly, Peter Kutz, Yining Karl Li, and Daniel Teece. 2018. The Design and Evolution of Disney’s Hyperion Renderer. ACM Transactions on Graphics (TOG) 37, 3 (2018), 33. https://doi.org/10.1145/3182159
Alexander Keller, Luca Fascione, Marcos Fajardo, Iliyan Georgiev, Per H Christensen, Johannes Hanika, Christian Eisenacher, and Gregory Nichols. 2015. The path tracing revolution in the movie industry.. In SIGGRAPH Courses. 24–1. https: //doi.org/10.1145/2776880.2792699
Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, and Jan Novák. 2018. Denoising with kernel prediction and asymmetric loss functions. ACM Transactions on Graphics (TOG) 37, 4 (2018), 124. https://doi.org/10.1145/3197517.3201388
