COMPOSITION
DESIGN
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The Hybrids by Phil Langer – hyper-realistic AI-generated human animal portraitsRead 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 timesRead 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. (more…)
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SecretWeapons MixBox – a practical library for paint-like digital color mixingRead more: SecretWeapons MixBox – a practical library for paint-like digital color mixingInternally, Mixbox treats colors as real-life pigments using the Kubelka & Munk theory to predict realistic color behavior. https://scrtwpns.com/mixbox/painter/ https://scrtwpns.com/mixbox.pdf https://github.com/scrtwpns/mixbox https://scrtwpns.com/mixbox/docs/ 
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mmColorTarget – Nuke Gizmo for color matching a MacBeth chartRead more: mmColorTarget – Nuke Gizmo for color matching a MacBeth charthttps://www.marcomeyer-vfx.de/posts/2014-04-11-mmcolortarget-nuke-gizmo/ https://www.marcomeyer-vfx.de/posts/mmcolortarget-nuke-gizmo/ https://vimeo.com/9.1652466e+07 https://www.nukepedia.com/gizmos/colour/mmcolortarget 
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THOMAS MANSENCAL – The Apparent Simplicity of RGB RenderingRead more: THOMAS MANSENCAL – The Apparent Simplicity of RGB Renderinghttps://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. 
LIGHTING
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Magnific.ai Relight – change the entire lighting of a sceneRead more: Magnific.ai Relight – change the entire lighting of a sceneIt’s a new Magnific spell that allows you to change the entire lighting of a scene and, optionally, the background with just: 1/ A prompt OR 
 2/ A reference image OR
 3/ A light map (drawing your own lights)https://x.com/javilopen/status/1805274155065176489 
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Neural Microfacet Fields for Inverse RenderingRead more: Neural Microfacet Fields for Inverse Renderinghttps://half-potato.gitlab.io/posts/nmf/ 
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What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…Read more: What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…RASTERIZATION 
 Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution. In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium. With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing. Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices. Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel. The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization. There are also multiple problems: - If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice. 
- The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing. 
- You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view) 
- Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order) 
 
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