https://blog.adobe.com/en/publish/2023/10/11/max-sneaks-2023
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Capitalism is the root cause of our ecological crisis and the key barrier to solving it.
There are two reasons for this. Firstly, capitalism cannot exist without economic growth, and economic growth is the main reason why our emissions have been increasing over the last 30 years and further growth will make it impossible to decarbonise in time to avoid activating tipping points.
Secondly, our minds have been shaped by capitalism and it is stopping us from seeing both the role capitalism plays in the cause of climate change and the full scope of solutions available to address the crisis. Social psychologist, Professor Harald Welzer, sums this up well, describing economic growth of industrial societies as “enshrined in business and politics, but also in the psychological structure of the people who grow up in such societies.”
https://erinremblance.substack.com/p/decapitalising-our-minds-the-key
There are six key ways in which capitalism shapes our minds. Under capitalism we, collectively, believe:
This ‘capitalisation’ of our minds creates a barrier to solving the climate crisis in many ways, including:
Unsurprisingly, the mindset we need to address the climate crisis is the exact opposite of the mindset described above.
Intel Open Image Denoise is an open source library of high-performance, high-quality denoising filters for images rendered with ray tracing. Intel Open Image Denoise is part of the Intel® oneAPI Rendering Toolkit and is released under the permissive Apache 2.0 license.
The purpose of Intel Open Image Denoise is to provide an open, high-quality, efficient, and easy-to-use denoising library that allows one to significantly reduce rendering times in ray tracing based rendering applications. It filters out the Monte Carlo noise inherent to stochastic ray tracing methods like path tracing, reducing the amount of necessary samples per pixel by even multiple orders of magnitude (depending on the desired closeness to the ground truth). A simple but flexible C/C++ API ensures that the library can be easily integrated into most existing or new rendering solutions.
At the heart of the Intel Open Image Denoise library is a collection of efficient deep learning based denoising filters, which were trained to handle a wide range of samples per pixel (spp), from 1 spp to almost fully converged. Thus it is suitable for both preview and final-frame rendering. The filters can denoise images either using only the noisy color (beauty) buffer, or, to preserve as much detail as possible, can optionally utilize auxiliary feature buffers as well (e.g. albedo, normal). Such buffers are supported by most renderers as arbitrary output variables (AOVs) or can be usually implemented with little effort.
https://www.cbc.ca/listen/live-radio/1-50-q/clip/16014382-tom-hanks
Two-time Oscar winner Tom Hanks (Forrest Gump, Philadelphia, A League of Their Own) on his debut novel “The Making of Another Major Motion Picture Masterpiece,” the insecurities he’s felt throughout his career, and what drives his passion for filmmaking when it feels like “the odds are stacked against you.”
Nothing comes easy if you learnt all through mistakes…
The change will happen sometime next year and will charge some users on a per-seat model, similar to Photoshop pricing.
Game developers using Unreal Engine won’t be affected and will continue to pay for a license based on a royalty model. However, users in industries like film or automotive will be moved to per-seat pricing, meaning they’ll be charged for the subscription the same way someone might pay for Photoshop.
https://www.theverge.com/2023/10/5/23905082/epic-unreal-engine-pricing-change-film-automotive
https://neuralradiancefields.io/luma-interactive-scenes-announced/
“…these are in fact Gaussian Splats that are being run and it’s a proprietary iteration of the original Inria paper. They hybridize the performance gain of realtime rendering with Gaussian Splatting with robust cloud based rendering that’s already widely being used in commercial applications. This has been in the works for a while over at Luma and I had the opportunity to try out some of my datasets on their new method.”
MICHAEL RUBLOFF
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