COMPOSITION
DESIGN
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The Hybrids by Phil Langer – hyper-realistic AI-generated human animal portraits
Read more: The Hybrids by Phil Langer – hyper-realistic AI-generated human animal portraitshttps://www.reddit.com/r/aiArt/comments/1azepd6/hybrid_portraits_by_phil_langer/
https://www.thehybridportraits.com/
https://www.instagram.com/hybridportraits/
COLOR
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No one could see the colour blue until modern times
Read more: No one could see the colour blue until modern timeshttps://www.businessinsider.com/what-is-blue-and-how-do-we-see-color-2015-2
The way 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-colorsEvery 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/True blue hues are rare in the natural world because synthesizing pigments that absorb longer-wavelength light (reds and yellows) while reflecting shorter-wavelength blue light requires exceptionally elaborate molecular structures—biochemical feats that most plants and animals simply don’t undertake.
When you gaze at a blueberry’s deep blue surface, you’re actually seeing structural coloration rather than a true blue pigment. A fine, waxy bloom on the berry’s skin contains nanostructures that preferentially scatter blue and violet light, giving the fruit its signature blue sheen even though its inherent pigment is reddish.
Similarly, many of nature’s most striking blues—like those of blue jays and morpho butterflies—arise not from blue pigments but from microscopic architectures in feathers or wing scales. These tiny ridges and air pockets manipulate incoming light so that blue wavelengths emerge most prominently, creating vivid, angle-dependent colors through scattering rather than pigment alone.
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THOMAS MANSENCAL – The Apparent Simplicity of RGB Rendering
Read more: THOMAS MANSENCAL – The Apparent Simplicity of RGB Rendering
https://thomasmansencal.substack.com/p/the-apparent-simplicity-of-rgb-rendering
The primary goal of physically-based rendering (PBR) is to create a simulation that accurately reproduces the imaging process of electro-magnetic spectrum radiation incident to an observer. This simulation should be indistinguishable from reality for a similar observer.
Because a camera is not sensitive to incident light the same way than a human observer, the images it captures are transformed to be colorimetric. A project might require infrared imaging simulation, a portion of the electro-magnetic spectrum that is invisible to us. Radically different observers might image the same scene but the act of observing does not change the intrinsic properties of the objects being imaged. Consequently, the physical modelling of the virtual scene should be independent of the observer.
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What causes color
Read more: What causes colorwww.webexhibits.org/causesofcolor/5.html
Water itself has an intrinsic blue color that is a result of its molecular structure and its behavior.
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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.
LIGHTING
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Gamma correction
Read more: Gamma correction
http://www.normankoren.com/makingfineprints1A.html#Gammabox
https://en.wikipedia.org/wiki/Gamma_correction
http://www.photoscientia.co.uk/Gamma.htm
https://www.w3.org/Graphics/Color/sRGB.html
http://www.eizoglobal.com/library/basics/lcd_display_gamma/index.html
https://forum.reallusion.com/PrintTopic308094.aspx
Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships.
Three main types:
– Image Gamma encoded in images
– Display Gammas encoded in hardware and/or viewing time
– System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.
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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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StudioBinder.com – CRI color rendering index
Read more: StudioBinder.com – CRI color rendering indexwww.studiobinder.com/blog/what-is-color-rendering-index
“The Color Rendering Index is a measurement of how faithfully a light source reveals the colors of whatever it illuminates, it describes the ability of a light source to reveal the color of an object, as compared to the color a natural light source would provide. The highest possible CRI is 100. A CRI of 100 generally refers to a perfect black body, like a tungsten light source or the sun. ”
www.pixelsham.com/2021/04/28/types-of-film-lights-and-their-efficiency
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