Category: production

 

https://roboflow.com/

 

An open-source swiss army knife for everything Computer Vision.

 

https://customersuccess.autodesk.com/learning/course/introduction-to-shotgrid

 

 

Learn about ShotGrid’s basic capabilities and functionality in this introductory course. Set up your account, gain an understanding of the structure of data within ShotGrid, learn to navigate ShotGrid, determine your role, including what you can and cannot do, and customize the view of on-screen data.

Steve Wright

https://www.linkedin.com/pulse/why-oh-premultiply-steve-wright/

 

James Pratt

https://jamesprattvfx.wordpress.com/2018/11/08/premult-unpremult/

 

The simple definition of premult is to multiply the alpha and the RGB of the input together.

Un-Premult suggests that this does the opposite operation to the premult node. Therefore instead of multiplying the RGB values by the alpha, it divides instead.

 

Alan Martinez

https://www.linkedin.com/posts/alan-martinez-1a7a60234_unpremult-and-premult-are-terms-used-activity-7089270470889394176-OVXE

 

“Unpremult” and “premult” are terms used in digital compositing that are relevant for both those working with computer-generated graphics (CG) and those working with live-action plates.

 

“Unpremult” is short for “unpremultiply” and refers to the action of undoing the multiplication of a pixel by its alpha value. It is commonly used to avoid halos or unwanted edges when combining images. (This by making sure that edits to a layer are added independently from edges’ opacity levels.)

 

“Premult” is short for “premultiply” and is the opposite process of “unpremult.” In this case, each pixel in an image is multiplied by its alpha value.
In simple terms, premult crops the RGB by its alpha, while unpremult does the opposite.

 

It’s important to perform color corrections on CG renders in a sort of sandwich approach. First, divide the image to extend the edges fully of the RGB channels. Then, apply the necessary color corrections. Finally, pre-multiply the image again to avoid artifacts on the edges.

 

Typically, most 3D rendered images are premultiplied. As a rule of thumb, if the background is black or even just very dark, the image may be premultiplied. Additionally, most of the time, the 3D render has antialiasing in the edges.

 

Aaron Strasbourg

https://www.aaronstrasbourgvfx.com/post/2017/06/23/002-unpremult-and-premult

https://help.autodesk.com/view/ARNOL/ENU/?guid=arnold_user_guide_ac_denoising_html

 

 

AOV denoising: While all denoisers work on arbitrary AOVs, not all denoisers guarantee that the denoised AOVs composite together to match the denoised beauty. The AOV column indicates whether a denoiser has robust AOV denoising and can produce a result where denoised_AOV_1 + denoised_AOV_2 + … + denoised_AOV_N = denoised_Beauty.

 

OptiX™ Denoiser imager

This imager is available as a post-processing effect. The imager also exposes additional controls for clamping and blending the result. It is based on Nvidia AI technology and is integrated into Arnold for use with IPR and look dev. The OptiX™ denoiser is meant to be used during IPR (so that you get a very quickly denoised image as you’re moving the camera and making other adjustments).

 

OIDN Denoiser imager

The OIDN denoiser (based on Intel’s Open Image Denoise technology) is available as a post-processing effect. It is integrated into Arnold for use with IPR as an imager (so that you get a very quickly denoised image as you’re moving the camera and making other adjustments).

 

Arnold Denoiser (Noice)

The Arnold Denoiser (Noice) can be run from a dedicated UI, exposed in the Denoiser, or as an imager, you will need to render images out first via the Arnold EXR driver with variance AOVs enabled. It is also available as a stand-alone program (noice.exe).

This imager is available as a post-processing effect. You can automatically denoise images every time you render a scene, edit the denoising settings and see the resulting image directly in the render view. It favors quality over speed and is, therefore, more suitable for high-quality final frame denoising and animation sequences.
Note:

imager_denoiser_noice does not support temporal denoising (required for denoising an animation).

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