Carl S. Marshall
ABSTRACT
• Give insights into closing the gap between taking a research neural model to deployment
• Understand the challenges in development, training, deployment, and iteration of neural networks for rendering
• Show practical use cases, tools, and networks to start your path toward neural rendering in production software
Supplemental Material
Available for Download
- Image Denoising - https://studios.disneyresearch.com/2018/07/30/denoising-with-kernel-prediction-and-asymmetric-loss-functions/Google Scholar
- Scene relighting - haGoogle Scholar
- Compositional Neural Scene representation - https://iannovak.info/publications/CNSR/CNSR.pdfGoogle Scholar
- DLSS - https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-big-leap-in-ai-rendering/Google Scholar
Index Terms
Practical machine learning for rendering: from research to deployment
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