COMPOSITION
DESIGN
COLOR
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No one could see the colour blue until modern times
https://www.businessinsider.com/what-is-blue-and-how-do-we-see-color-2015-2
The way that humans see the world… until we have a way to describe something, even something so fundamental as a colour, we may not even notice that something it’s there.
Ancient languages didn’t have a word for blue — not Greek, not Chinese, not Japanese, not Hebrew, not Icelandic cultures. And without a word for the colour, there’s evidence that they may not have seen it at all.
https://www.wnycstudios.org/story/211119-colors
Every language first had a word for black and for white, or dark and light. The next word for a colour to come into existence — in every language studied around the world — was red, the colour of blood and wine.
After red, historically, yellow appears, and later, green (though in a couple of languages, yellow and green switch places). The last of these colours to appear in every language is blue.
The only ancient culture to develop a word for blue was the Egyptians — and as it happens, they were also the only culture that had a way to produce a blue dye.
https://mymodernmet.com/shades-of-blue-color-history/
https://www.msn.com/en-us/news/technology/scientists-recreate-lost-recipes-for-a-5-000-year-old-egyptian-blue-dye/ar-AA1FXcj1
Assessment of process variability and color in synthesized and ancient Egyptian blue pigments | npj Heritage ScienceThe approximately 5,000-year-old dye wasn’t a single color, but instead encompassed a range of hues, from deep blues to duller grays and greens. Artisans first crafted Egyptian blue during the Fourth Dynasty (roughly 2613 to 2494 BCE) from recipes reliant on calcium-copper silicate. These techniques were later adopted by Romans in lieu of more expensive materials like lapis lazuli and turquoise. But the additional ingredient lists were lost to history by the time of the Renaissance.
McCloy’s team confirmed that cuprorivaite—the naturally occurring mineral equivalent to Egyptian blue—remains the primary color influence in each hue. Despite the presence of other components, Egyptian blue appears as a uniform color after the cuprorivaite becomes encased in colorless particles such as silicate during the heating process.Considered to be the first ever synthetically produced color pigment, Egyptian blue (also known as cuprorivaite) was created around 2,200 B.C. It was made from ground limestone mixed with sand and a copper-containing mineral, such as azurite or malachite, which was then heated between 1470 and 1650°F. The result was an opaque blue glass which then had to be crushed and combined with thickening agents such as egg whites to create a long-lasting paint or glaze.
If you think about it, blue doesn’t appear much in nature — there aren’t animals with blue pigments (except for one butterfly, Obrina Olivewing, all animals generate blue through light scattering), blue eyes are rare (also blue through light scattering), and blue flowers are mostly human creations. There is, of course, the sky, but is that really blue?
So before we had a word for it, did people not naturally see blue? Do you really see something if you don’t have a word for it?
A researcher named Jules Davidoff traveled to Namibia to investigate this, where he conducted an experiment with the Himba tribe, who speak a language that has no word for blue or distinction between blue and green. When shown a circle with 11 green squares and one blue, they couldn’t pick out which one was different from the others.
When looking at a circle of green squares with only one slightly different shade, they could immediately spot the different one. Can you?
Davidoff says that without a word for a colour, without a way of identifying it as different, it’s much harder for us to notice what’s unique about it — even though our eyes are physically seeing the blocks it in the same way.
Further research brought to wider discussions about color perception in humans. Everything that we make is based on the fact that humans are trichromatic. The television only has 3 colors. Our color printers have 3 different colors. But some people, and in specific some women seemed to be more sensible to color differences… mainly because they’re just more aware or – because of the job that they do.
Eventually this brought to the discovery of a small percentage of the population, referred to as tetrachromats, which developed an extra cone sensitivity to yellow, likely due to gene modifications.
The interesting detail about these is that even between tetrachromats, only the ones that had a reason to develop, label and work with extra color sensitivity actually developed the ability to use their native skills.
So before blue became a common concept, maybe humans saw it. But it seems they didn’t know they were seeing it.
If you see something yet can’t see it, does it exist? Did colours come into existence over time? Not technically, but our ability to notice them… may have…
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Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color picking
Read more: Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color pickinghttps://bottosson.github.io/misc/colorpicker
https://bottosson.github.io/posts/colorpicker/
https://www.smashingmagazine.com/2024/10/interview-bjorn-ottosson-creator-oklab-color-space/
One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.
There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.
I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.
IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.
In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.
By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.
Today HSL and HSV are most commonly used together with the sRGB color space.
One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.
The main drawback on the other hand is that their properties don’t match human perception particularly well.
Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.
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Capturing textures albedo
Read more: Capturing textures albedoBuilding a Portable PBR Texture Scanner by Stephane Lb
http://rtgfx.com/pbr-texture-scanner/How To Split Specular And Diffuse In Real Images, by John Hable
http://filmicworlds.com/blog/how-to-split-specular-and-diffuse-in-real-images/Capturing albedo using a Spectralon
https://www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdfReal_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
Spectralon is a teflon-based pressed powderthat comes closest to being a pure Lambertian diffuse material that reflects 100% of all light. If we take an HDR photograph of the Spectralon alongside the material to be measured, we can derive thediffuse albedo of that material.
The process to capture diffuse reflectance is very similar to the one outlined by Hable.
1. We put a linear polarizing filter in front of the camera lens and a second linear polarizing filterin front of a modeling light or a flash such that the two filters are oriented perpendicular to eachother, i.e. cross polarized.
2. We place Spectralon close to and parallel with the material we are capturing and take brack-eted shots of the setup7. Typically, we’ll take nine photographs, from -4EV to +4EV in 1EVincrements.
3. We convert the bracketed shots to a linear HDR image. We found that many HDR packagesdo not produce an HDR image in which the pixel values are linear. PTGui is an example of apackage which does generate a linear HDR image. At this point, because of the cross polarization,the image is one of surface diffuse response.
4. We open the file in Photoshop and normalize the image by color picking the Spectralon, filling anew layer with that color and setting that layer to “Divide”. This sets the Spectralon to 1 in theimage. All other color values are relative to this so we can consider them as diffuse albedo.
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Björn Ottosson – OKlch color space
Björn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.
The OK in OKLCH stands for Optimal Color.
- L: Lightness (the perceived brightness of the color)
- C: Chroma (the intensity or saturation of the color)
- H: Hue (the actual color, such as red, blue, green, etc.)
Also read:
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Akiyoshi Kitaoka – Surround biased illumination perception
Read more: Akiyoshi Kitaoka – Surround biased illumination perceptionhttps://x.com/AkiyoshiKitaoka/status/1798705648001327209
The left face appears whitish and the right one blackish, but they are made up of the same luminance.
https://community.wolfram.com/groups/-/m/t/3191015
Illusory staircase Gelb effect
https://www.psy.ritsumei.ac.jp/akitaoka/illgelbe.html
LIGHTING
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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
Read more: DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ballhttps://diffusionlight.github.io/
https://github.com/DiffusionLight/DiffusionLight
https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme
https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
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Terminators and Iron Men: HDRI, Image-based lighting and physical shading at ILM – Siggraph 2010
Read more: Terminators and Iron Men: HDRI, Image-based lighting and physical shading at ILM – Siggraph 2010 -
Convert between light exposure and intensity
Read more: Convert between light exposure and intensityimport math,sys def Exposure2Intensity(exposure): exp = float(exposure) result = math.pow(2,exp) print(result) Exposure2Intensity(0) def Intensity2Exposure(intensity): inarg = float(intensity) if inarg == 0: print("Exposure of zero intensity is undefined.") return if inarg < 1e-323: inarg = max(inarg, 1e-323) print("Exposure of negative intensities is undefined. Clamping to a very small value instead (1e-323)") result = math.log(inarg, 2) print(result) Intensity2Exposure(0.1)
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Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesis
Read more: Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesishttps://srameo.github.io/projects/le3d/
LE3D is a method for real-time HDR view synthesis from RAW images. It is particularly effective for nighttime scenes.
https://github.com/Srameo/LE3D
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RawTherapee – a free, open source, cross-platform raw image and HDRi processing program
5.10 of this tool includes excellent tools to clean up cr2 and cr3 used on set to support HDRI processing.
Converting raw to AcesCG 32 bit tiffs with metadata. -
Open Source Nvidia Omniverse
Read more: Open Source Nvidia Omniverseblogs.nvidia.com/blog/2019/03/18/omniverse-collaboration-platform/
developer.nvidia.com/nvidia-omniverse
An open, Interactive 3D Design Collaboration Platform for Multi-Tool Workflows to simplify studio workflows for real-time graphics.
It supports Pixar’s Universal Scene Description technology for exchanging information about modeling, shading, animation, lighting, visual effects and rendering across multiple applications.
It also supports NVIDIA’s Material Definition Language, which allows artists to exchange information about surface materials across multiple tools.
With Omniverse, artists can see live updates made by other artists working in different applications. They can also see changes reflected in multiple tools at the same time.
For example an artist using Maya with a portal to Omniverse can collaborate with another artist using UE4 and both will see live updates of each others’ changes in their application.
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Light and Matter : The 2018 theory of Physically-Based Rendering and Shading by Allegorithmic
Read more: Light and Matter : The 2018 theory of Physically-Based Rendering and Shading by Allegorithmicacademy.substance3d.com/courses/the-pbr-guide-part-1
academy.substance3d.com/courses/the-pbr-guide-part-2
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