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AI Learns Noise Filtering For Photorealistic Videos | Two Minute Papers #215
About this video
A Two Minute Papers episode on NVIDIA's recurrent denoising autoencoder, which cleans up noisy Monte Carlo path-traced image sequences. It shows how a neural network can produce usable renders from very few samples.
Why it appears in the archive
Cited as an accessible explanation of AI denoising, which underpins real-time ray tracing on RTX hardware.
AI-generated summary and labels from the source dataset; verify important details against the video. Source & credits.
From the Hacker News discussion
Disney actually does a lot of research combining the world of graphics with deep learning. Some examples that you might appreciate (from the excellent channel "Two Minute Papers"): . "Disney's AI Learns To Render Clouds" [0] . "AI Learns Noise Filtering For Photorealistic Videos" [1] [0] [LINK] [1] [VIDEO]
Read original comment ↗It's only briefly mentioned, but NVIDIA's recurrent denoising autoencoder is pretty amazing. [LINK] Two Minute Papers did a nice explanation of it: [VIDEO]
Read original comment ↗I don't think your estimations take into account the massive gains from AI denoising techniques such as [VIDEO]
Read original comment ↗This uses a neural network [VIDEO] video results start around 1:15 This one does not use a neural network: [LINK]
Read original comment ↗Deep learning denoiser. I don't know if NVIDIA came up with some marketing term for it. It's what allows RTX to produce meaningful images at all despite the renderer running at 0.5-2 samples per pixel which looks little better than random noise. I kind of assumed they'd call it DLDN because they called their deep learning supersampling DLSS. I guess not, go figure. [VIDEO]
Read original comment ↗