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https://github.com/AcademySoftwareFoundation/OpenRV
https://adsknews.autodesk.com/news/rv-open-source
“Autodesk is committed to helping creators envision a better world, and having access to great tools allows them do just that. So we are making RV, our Sci-Tech award-winning media review and playback software, open source. Code contributions from RV along with DNEG’s xStudio and Sony Pictures Imageworks’ itview will shape the Open Review Initiative, the Academy Software Foundation’s (ASWF) newest sandbox project to build a unified, open source toolset for playback, review, and approval. ”
Texel density (also referred to as pixel density or texture density) is a measurement unit used to make asset textures cohesive compared to each other throughout your entire world.
It’s measured in pixels per centimeter (ie: 2.56px/cm) or pixels per meter (ie: 256px/m).
https://www.beyondextent.com/deep-dives/deepdive-texeldensity
https://github.com/proceduralit/StableDiffusion_Houdini
https://github.com/proceduralit/StableDiffusion_Houdini/wiki/
This is a Houdini HDA that submits the render output as the init_image and with getting help from PDG, enables artists to easily define variations on the Stable Diffusion parameters like Sampling Method, Steps, Prompt Strength, and Noise Strength.
Right now DreamStudio is the only public server that the HDA is supporting. So you need to have an account there and connect the HDA to your account.
DreamStudio: https://beta.dreamstudio.ai/membership
https://www.zumolabs.ai/post/what-is-neural-rendering
“The key concept behind neural rendering approaches is that they are differentiable. A differentiable function is one whose derivative exists at each point in the domain. This is important because machine learning is basically the chain rule with extra steps: a differentiable rendering function can be learned with data, one gradient descent step at a time. Learning a rendering function statistically through data is fundamentally different from the classic rendering methods we described above, which calculate and extrapolate from the known laws of physics.”
The Cattery is a library of free third-party machine learning models converted to .cat files to run natively in Nuke, designed to bridge the gap between academia and production, providing all communities access to different ML models that all run in Nuke. Users will have access to state-of-the-art models addressing segmentation, depth estimation, optical flow, upscaling, denoising, and style transfer, with plans to expand the models hosted in the future.
https://www.foundry.com/insights/machine-learning/the-artists-guide-to-cattery
https://community.foundry.com/cattery
Display Referred it is tied to the target hardware, as such it bakes color requirements into every type of media output request.
Scene Referred uses a common unified wide gamut and targeting audience through CDL and DI libraries instead.
So that color information stays untouched and only “transformed” as/when needed.
Sources:
– Victor Perez – Color Management Fundamentals & ACES Workflows in Nuke
– https://z-fx.nl/ColorspACES.pdf
– Wicus
https://github.com/autodesk/Aurora
Goals for Aurora
Features
After 12 years developing and supporting Bokeh we are excited to announce the product has found a new home with Foundry.
https://peregrinelabs.com/blogs/news/bokeh-has-a-new-home
https://www.geogebra.org/calculator
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