Cinema

Tidbits A month ago, when I was looking for books about Industrial Light and Magic, I found a treasure book “The Art of Film Magic”, which focuses on Weta Workshop’s film special effects achievements in the last 20 years, I wanted to buy this set of books through domestic purchase channels, but finally found that some of the contents of the book are in conflict with Chinese laws, and the conflicting parts of the book was torn from the pages by a third party. So I found Weta Workshop’s official store and bought this set of books directly from Europe, and it arrived yesterday through UPS. ...

reference http://alvyray.com/Pixar/documents/Pixar_ImageComputer.pdf https://ohiostate.pressbooks.pub/app/uploads/sites/45/2017/09/pixar-image-processor.pdf Pixar Image Computer What inspired me to write this article is that last month, March, I took a quick look at The Renderman Companion A Program’s Guide To Realistic Computer Graphics, which mainly describes how to use the Renderman C API interface to achieve 3D image rendering. The book was published in 1989, when Pixar had developed the Renderman rendering interface standard and implemented the PRMan PhotoRealistic Renderman implementation that is now widely used in the film industry. One of the interesting things is that in 1986 Pixar also developed a computer graphics hardware system similar to today’s Nvidia general-purpose graphics cards - Pixar Image Computer. ...

Paper Deep Learned Super Resolution for Feature Film Production Learning Recently I have been learning about 3D modeling rendering and production, and I saw a paper about Pixar’s release in 2020, the main content is about how Pixar’s internal engineers and developers use deep learning to improve the resolution of rendered images, fine rendering of a 4k image frame may consume a lot of capital cost, if the final rendering of 2k resolution image then use deep neural network to boost it to 4k, the image is more detailed and richer, then it can help companies reduce a lot of the cost of rendering. Unlike the previous image super-resolution enhancement model, the film industry typically uses images that contain data with high dynamic range of lighting, and true reproduction of color, which makes the current mainstream super-resolution models simply unusable in the film domain. This is an area of interest to me at the moment, as using deep learning for industrial applications is more practical than theoretical. ...

The BMPCC camera I purchased in 2017 had an oxidation problem with the low-pass filter, and the images it captured were smudged inside. I used my free time this afternoon to replace the low-pass filter on this BlackMagic Pocket Cinema Camera. I love this camera, because I’ve shot a lot of interesting work with it since I purchased it. Appreciation of works shot with BMPCC (Created in 2018.12): ...

Reference articles https://docs.blender.org/manual/en/latest/render/color_management.html https://rmanwiki.pixar.com/display/REN24/Color+Management https://www.arri.com/en/learn-help/learn-help-camera-system/camera-workflow/image-science/aces https://github.com/colour-science/OpenColorIO-Configs https://acescentral.com/knowledge-base-2/using-aces-reference-images/ https://opencolorio.readthedocs.io/en/latest/configurations/aces_1.0.3.html Talk about Gamma Correction Table[x^2.2, {x, 0, 1, 0.1}] {0., 0.00630957, 0.0289912, 0.0707403, 0.133209, 0.217638, 0.325037, \ 0.456263, 0.612066, 0.79311, 1.} Table[x, {x, 0, 1, 0.1}] {0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.} Table[x^(1/2.2), {x, 0, 1, 0.1}] {0., 0.351119, 0.481157, 0.578533, 0.659353, 0.72974, 0.792793, \ 0.850335, 0.903545, 0.953238, 1.} #^2.2 & /@ Table[x^(1/2.2), {x, 0, 1, 0.1}] {0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.} To get a preliminary understanding of the gamma function, it can be seen from the above expression that the transformation obtained by the gamma function 2.2, we can keep the input and output in a linear relationship by the inverse gamma function 1/2.2. ...