At Chaos, we're committed to pushing the boundaries of what's possible in the world of computer graphics technologies. That's why we've established the Innovation Lab, a dedicated initiative led by Chaos co-founder Vladimir Koylazov.
The Innovation Lab encourages Chaos employees around the world to share ideas and research in rendering and related technologies, including offline and real-time rendering, machine learning, and artificial intelligence (AI). We also partner with academic institutions and other research and development hubs in computer graphics technologies to test new concepts and contribute to the long-term vision of Chaos’ products and to the future of the industry.
Innovation doesn't happen without collaboration. Whether you're an artist, industry leader, or academic researcher, you're welcome to contribute to development discussions and help solve key computer graphics challenges.
Our products and solutions have been successful because they have consistently offered innovative and efficient ways for our users to design and visualize. Technology is developing very quickly and our goal is to continue providing the right tools, technologies, and expertise so that architects, designers and artists can be more effective and creative.
Vladimir Koylazov, Co-founder, Chaos
A product of the Chaos Innovation Lab, Chaos Arena (formerly Project Arena) is the first-ever real-time path-tracing solution for in-camera visual effects (ICVFX) on virtual production stages.
Extensively tested across diverse stage setups and configurations around the world, including the making of Ray Tracing FTW, Arena has officially transitioned out of beta and is now production-ready.
Journal article by Asen Atanasov and Vladimir Koylazov
Journal article by Asen Atanasov, Vladimir Koylazov, Rossen Dimov, Alexander Wilkie.
Journal article by Asen Atanasov, Alexander Wilkie, Vladimir Koylazov, Jaroslav Křivánek.
Article by Asen Atanasov, Jaroslav Křivánek, Vladimir Koylazov, Alexander Wilkie.
This paper presents a production-ready approach for efficient environment light sampling which takes visibility into account.
This internal report attempts to measure the efficiency of the V-Ray denoiser in reducing the Monte Carlo error of renders.
The paper describes the algorithm behind V-Ray Stochastic Flakes Material.
This paper describes the work we did to derive calculations of the Smith shadowing function for different values of the highlight tail falloff of the GTR BRDF in V-Ray 3.20. See below for the PDF and source code.
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