Disney's AI Learns To Render Clouds | Two Minute Papers #204 | Summary and Q&A

TL;DR

This in-house Disney paper explores how neural networks can be trained to capture the appearance of clouds.

Key Insights

• ⛈️ Rendering realistic clouds involves volumetric path tracing, which requires simulating millions of light paths with scattering events.
• 😶‍🌫️ The traditional rendering approach can take up to 30 hours to render an image of bright clouds.
• ⌛ The proposed neural network approach significantly reduces rendering times to seconds or minutes by learning in-scattered radiance.
• 😶‍🌫️ The neural network is trained on a dataset of 75 clouds to capture a wide variety of cases.
• 👻 The technique allows for interactive editing of scattering parameters without the need for lengthy trial and error phases.
• ❓ The rendered images using deep scattering are indistinguishable from reality.
• 🥶 The technique is temporally stable, ensuring flicker-free animation rendering.

Transcript

Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. This is a fully in-house Disney paper on how to teach a neural network to capture the appearance of clouds. This topic is one of my absolute favorites because it is in the intersection of the two topics I love most - computer graphics and machine learning. Hell yeah! General... Read More

Questions & Answers

Q: What challenges are involved in rendering clouds realistically?

Rendering clouds realistically requires simulating volumetric path tracing, which involves simulating millions of light paths with scattering events. This is computationally demanding and can take hours to render an image.

Q: How does the neural network approach improve rendering times?

The neural network is trained to learn in-scattered radiance, which eliminates the need to compute certain parts of the rendering process. This significantly reduces rendering times from hours to seconds or minutes.

Q: How was the neural network trained?

The neural network was trained on a dataset of 75 different clouds, including both procedurally generated and artist-drawn clouds. This exposure to a variety of cases helps the network learn to predict in-scattered radiance accurately.

Q: Does the deep scattering technique support different scattering models?

Yes, the deep scattering technique supports a variety of different scattering models, allowing for flexibility in capturing the appearance of clouds.

Summary & Key Takeaways

• Rendering clouds using light simulation programs requires volumetric path tracing, which involves simulating rays of light that penetrate surfaces and undergo scattering events.

• This computationally demanding process can take up to 30 hours to render an image of bright clouds.

• The paper proposes a hybrid approach where a neural network learns in-scattered radiance, significantly reducing rendering times to a matter of seconds.