COMPOSITION
DESIGN
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AI Dresses by MaryAnna
Read more: AI Dresses by MaryAnnahttps://www.linkedin.com/feed/update/urn:li:activity:7015985798567067648
Created by Discord user: @MaryAnna
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Mike Mitchell x Marvel x Mondo – Iconic portraits of Marvel’s huge stable of heroes and villains
Read more: Mike Mitchell x Marvel x Mondo – Iconic portraits of Marvel’s huge stable of heroes and villainshttps://mondoshop.com/blogs/gallery/16910155-mike-mitchell-x-marvel-x-mondo
https://time.com/69659/marvel-comics-mike-mitchell-artist-portraits/
COLOR
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3D Lighting Tutorial by Amaan Kram
Read more: 3D Lighting Tutorial by Amaan Kramhttp://www.amaanakram.com/lightingT/part1.htm
The goals of lighting in 3D computer graphics are more or less the same as those of real world lighting.
Lighting serves a basic function of bringing out, or pushing back the shapes of objects visible from the camera’s view.
It gives a two-dimensional image on the monitor an illusion of the third dimension-depth.But it does not just stop there. It gives an image its personality, its character. A scene lit in different ways can give a feeling of happiness, of sorrow, of fear etc., and it can do so in dramatic or subtle ways. Along with personality and character, lighting fills a scene with emotion that is directly transmitted to the viewer.
Trying to simulate a real environment in an artificial one can be a daunting task. But even if you make your 3D rendering look absolutely photo-realistic, it doesn’t guarantee that the image carries enough emotion to elicit a “wow” from the people viewing it.
Making 3D renderings photo-realistic can be hard. Putting deep emotions in them can be even harder. However, if you plan out your lighting strategy for the mood and emotion that you want your rendering to express, you make the process easier for yourself.
Each light source can be broken down in to 4 distinct components and analyzed accordingly.
· Intensity
· Direction
· Color
· SizeThe overall thrust of this writing is to produce photo-realistic images by applying good lighting techniques.
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A Brief History of Color in Art
Read more: A Brief History of Color in Artwww.artsy.net/article/the-art-genome-project-a-brief-history-of-color-in-art
Of all the pigments that have been banned over the centuries, the color most missed by painters is likely Lead White.
This hue could capture and reflect a gleam of light like no other, though its production was anything but glamorous. The 17th-century Dutch method for manufacturing the pigment involved layering cow and horse manure over lead and vinegar. After three months in a sealed room, these materials would combine to create flakes of pure white. While scientists in the late 19th century identified lead as poisonous, it wasn’t until 1978 that the United States banned the production of lead white paint.
More reading:
www.canva.com/learn/color-meanings/https://www.infogrades.com/history-events-infographics/bizarre-history-of-colors/
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Scientists claim to have discovered ‘new colour’ no one has seen before: Olo
Read more: Scientists claim to have discovered ‘new colour’ no one has seen before: Olohttps://www.bbc.com/news/articles/clyq0n3em41o
By stimulating specific cells in the retina, the participants claim to have witnessed a blue-green colour that scientists have called “olo”, but some experts have said the existence of a new colour is “open to argument”.
The findings, published in the journal Science Advances on Friday, have been described by the study’s co-author, Prof Ren Ng from the University of California, as “remarkable”.
(A) System inputs. (i) Retina map of 103 cone cells preclassified by spectral type (7). (ii) Target visual percept (here, a video of a child, see movie S1 at 1:04). (iii) Infrared cellular-scale imaging of the retina with 60-frames-per-second rolling shutter. Fixational eye movement is visible over the three frames shown.
(B) System outputs. (iv) Real-time per-cone target activation levels to reproduce the target percept, computed by: extracting eye motion from the input video relative to the retina map; identifying the spectral type of every cone in the field of view; computing the per-cone activation the target percept would have produced. (v) Intensities of visible-wavelength 488-nm laser microdoses at each cone required to achieve its target activation level.
(C) Infrared imaging and visible-wavelength stimulation are physically accomplished in a raster scan across the retinal region using AOSLO. By modulating the visible-wavelength beam’s intensity, the laser microdoses shown in (v) are delivered. Drawing adapted with permission [Harmening and Sincich (54)].
(D) Examples of target percepts with corresponding cone activations and laser microdoses, ranging from colored squares to complex imagery. Teal-striped regions represent the color “olo” of stimulating only M cones.
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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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What is a Gamut or Color Space and why do I need to know about CIE
Read more: What is a Gamut or Color Space and why do I need to know about CIE
http://www.xdcam-user.com/2014/05/what-is-a-gamut-or-color-space-and-why-do-i-need-to-know-about-it/
In video terms gamut is normally related to as the full range of colours and brightness that can be either captured or displayed.
Generally speaking all color gamuts recommendations are trying to define a reasonable level of color representation based on available technology and hardware. REC-601 represents the old TVs. REC-709 is currently the most distributed solution. P3 is mainly available in movie theaters and is now being adopted in some of the best new 4K HDR TVs. Rec2020 (a wider space than P3 that improves on visibke color representation) and ACES (the full coverage of visible color) are other common standards which see major hardware development these days.
To compare and visualize different solution (across video and printing solutions), most developers use the CIE color model chart as a reference.
The CIE color model is a color space model created by the International Commission on Illumination known as the Commission Internationale de l’Elcairage (CIE) in 1931. It is also known as the CIE XYZ color space or the CIE 1931 XYZ color space.
This chart represents the first defined quantitative link between distributions of wavelengths in the electromagnetic visible spectrum, and physiologically perceived colors in human color vision. Or basically, the range of color a typical human eye can perceive through visible light.
Note that while the human perception is quite wide, and generally speaking biased towards greens (we are apes after all), the amount of colors available through nature, generated through light reflection, tend to be a much smaller section. This is defined by the Pointer’s Chart.
In short. Color gamut is a representation of color coverage, used to describe data stored in images against available hardware and viewer technologies.
Camera color encoding from
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications
CIE 1976
http://bernardsmith.eu/computatrum/scan_and_restore_archive_and_print/scanning/
https://store.yujiintl.com/blogs/high-cri-led/understanding-cie1931-and-cie-1976
The CIE 1931 standard has been replaced by a CIE 1976 standard. Below we can see the significance of this.
People have observed that the biggest issue with CIE 1931 is the lack of uniformity with chromaticity, the three dimension color space in rectangular coordinates is not visually uniformed.
The CIE 1976 (also called CIELUV) was created by the CIE in 1976. It was put forward in an attempt to provide a more uniform color spacing than CIE 1931 for colors at approximately the same luminance
The CIE 1976 standard colour space is more linear and variations in perceived colour between different people has also been reduced. The disproportionately large green-turquoise area in CIE 1931, which cannot be generated with existing computer screens, has been reduced.
If we move from CIE 1931 to the CIE 1976 standard colour space we can see that the improvements made in the gamut for the “new” iPad screen (as compared to the “old” iPad 2) are more evident in the CIE 1976 colour space than in the CIE 1931 colour space, particularly in the blues from aqua to deep blue.
https://dot-color.com/2012/08/14/color-space-confusion/
Despite its age, CIE 1931, named for the year of its adoption, remains a well-worn and familiar shorthand throughout the display industry. CIE 1931 is the primary language of customers. When a customer says that their current display “can do 72% of NTSC,” they implicitly mean 72% of NTSC 1953 color gamut as mapped against CIE 1931.
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Anders Langlands – Render Color Spaces
Read more: Anders Langlands – Render Color Spaceshttps://www.colour-science.org/anders-langlands/
This page compares images rendered in Arnold using spectral rendering and different sets of colourspace primaries: Rec.709, Rec.2020, ACES and DCI-P3. The SPD data for the GretagMacbeth Color Checker are the measurements of Noburu Ohta, taken from Mansencal, Mauderer and Parsons (2014) colour-science.org.
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HDR and Color
Read more: HDR and Colorhttps://www.soundandvision.com/content/nits-and-bits-hdr-and-color
In HD we often refer to the range of available colors as a color gamut. Such a color gamut is typically plotted on a two-dimensional diagram, called a CIE chart, as shown in at the top of this blog. Each color is characterized by its x/y coordinates.
Good enough for government work, perhaps. But for HDR, with its higher luminance levels and wider color, the gamut becomes three-dimensional.
For HDR the color gamut therefore becomes a characteristic we now call the color volume. It isn’t easy to show color volume on a two-dimensional medium like the printed page or a computer screen, but one method is shown below. As the luminance becomes higher, the picture eventually turns to white. As it becomes darker, it fades to black. The traditional color gamut shown on the CIE chart is simply a slice through this color volume at a selected luminance level, such as 50%.
Three different color volumes—we still refer to them as color gamuts though their third dimension is important—are currently the most significant. The first is BT.709 (sometimes referred to as Rec.709), the color gamut used for pre-UHD/HDR formats, including standard HD.
The largest is known as BT.2020; it encompasses (roughly) the range of colors visible to the human eye (though ET might find it insufficient!).
Between these two is the color gamut used in digital cinema, known as DCI-P3.
sRGB
D65
LIGHTING
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Unity 3D resources
Read more: Unity 3D resources
http://answers.unity3d.com/questions/12321/how-can-i-start-learning-unity-fast-list-of-tutori.html
If you have no previous experience with Unity, start with these six video tutorials which give a quick overview of the Unity interface and some important features http://unity3d.com/support/documentation/video/
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What is the Light Field?
Read more: What is the Light Field?http://lightfield-forum.com/what-is-the-lightfield/
The light field consists of the total of all light rays in 3D space, flowing through every point and in every direction.
How to Record a Light Field
- a single, robotically controlled camera
- a rotating arc of cameras
- an array of cameras or camera modules
- a single camera or camera lens fitted with a microlens array
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IES Light Profiles and editing software
Read more: IES Light Profiles and editing softwarehttp://www.derekjenson.com/3d-blog/ies-light-profiles
https://ieslibrary.com/en/browse#ies
https://leomoon.com/store/shaders/ies-lights-pack
https://docs.arnoldrenderer.com/display/a5afmug/ai+photometric+light
IES profiles are useful for creating life-like lighting, as they can represent the physical distribution of light from any light source.
The IES format was created by the Illumination Engineering Society, and most lighting manufacturers provide IES profile for the lights they manufacture.
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Light properties
Read more: Light propertiesHow It Works – Issue 114
https://www.howitworksdaily.com/
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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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