COMPOSITION
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Photography basics: Depth of Field and compositionRead more: Photography basics: Depth of Field and compositionDepth of field is the range within which focusing is resolved in a photo. 
 Aperture has a huge affect on to the depth of field.Changing the f-stops (f/#) of a lens will change aperture and as such the DOF. f-stops are a just certain number which is telling you the size of the aperture. That’s how f-stop is related to aperture (and DOF). If you increase f-stops, it will increase DOF, the area in focus (and decrease the aperture). On the other hand, decreasing the f-stop it will decrease DOF (and increase the aperture). The red cone in the figure is an angular representation of the resolution of the system. Versus the dotted lines, which indicate the aperture coverage. Where the lines of the two cones intersect defines the total range of the depth of field. This image explains why the longer the depth of field, the greater the range of clarity. 
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Mastering Camera Shots and Angles: A Guide for FilmmakersRead more: Mastering Camera Shots and Angles: A Guide for Filmmakershttps://website.ltx.studio/blog/mastering-camera-shots-and-angles 1. Extreme Wide Shot  2. Wide Shot  3. Medium Shot  4. Close Up  5. Extreme Close Up  
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HuggingFace ai-comic-factory – a FREE AI Comic Book CreatorRead more: HuggingFace ai-comic-factory – a FREE AI Comic Book Creatorhttps://huggingface.co/spaces/jbilcke-hf/ai-comic-factory this is the epic story of a group of talented digital artists trying to overcame daily technical challenges to achieve incredibly photorealistic projects of monsters and aliens 
DESIGN
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Interactive Maps of Earthquakes around the worldRead more: Interactive Maps of Earthquakes around the worldhttps://ralucanicola.github.io/JSAPI_demos/earthquakes https://ralucanicola.github.io/JSAPI_demos/earthquakes-depth https://ralucanicola.github.io/JSAPI_demos/ridgecrest-earthquake https://ralucanicola.github.io/JSAPI_demos/last-earthquakes  
COLOR
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Practical Aspects of Spectral Data and LEDs in Digital Content Production and Virtual Production – SIGGRAPH 2022Read more: Practical Aspects of Spectral Data and LEDs in Digital Content Production and Virtual Production – SIGGRAPH 2022Comparison to the commercial side  https://www.ecolorled.com/blog/detail/what-is-rgb-rgbw-rgbic-strip-lights RGBW (RGB + White) LED strip uses a 4-in-1 LED chip made up of red, green, blue, and white. RGBWW (RGB + White + Warm White) LED strip uses either a 5-in-1 LED chip with red, green, blue, white, and warm white for color mixing. The only difference between RGBW and RGBWW is the intensity of the white color. The term RGBCCT consists of RGB and CCT. CCT (Correlated Color Temperature) means that the color temperature of the led strip light can be adjusted to change between warm white and white. Thus, RGBWW strip light is another name of RGBCCT strip. RGBCW is the acronym for Red, Green, Blue, Cold, and Warm. These 5-in-1 chips are used in supper bright smart LED lighting products 
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Brett Jones / Phil Reyneri (Lightform) / Philipp7pc: The study of Projection Mapping through ProjectorsRead more: Brett Jones / Phil Reyneri (Lightform) / Philipp7pc: The study of Projection Mapping through ProjectorsVideo Projection Tool Software 
 https://hcgilje.wordpress.com/vpt/https://www.projectorpoint.co.uk/news/how-bright-should-my-projector-be/ http://www.adwindowscreens.com/the_calculator/ heavym 
 https://heavym.net/en/MadMapper 
 https://madmapper.com/
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Mysterious animation wins best illusion of 2011 – Motion silencing illusionRead more: Mysterious animation wins best illusion of 2011 – Motion silencing illusionThe 2011 Best Illusion of the Year uses motion to render color changes invisible, and so reveals a quirk in our visual systems that is new to scientists. https://en.wikipedia.org/wiki/Motion_silencing_illusion “It is a really beautiful effect, revealing something about how our visual system works that we didn’t know before,” said Daniel Simons, a professor at the University of Illinois, Champaign-Urbana. Simons studies visual cognition, and did not work on this illusion. Before its creation, scientists didn’t know that motion had this effect on perception, Simons said. A viewer stares at a speck at the center of a ring of colored dots, which continuously change color. When the ring begins to rotate around the speck, the color changes appear to stop. But this is an illusion. For some reason, the motion causes our visual system to ignore the color changes. (You can, however, see the color changes if you follow the rotating circles with your eyes.) 
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Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color pickingRead 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.  
LIGHTING
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Composition – 5 tips for creating perfect cinematic lighting and making your work look stunningRead more: Composition – 5 tips for creating perfect cinematic lighting and making your work look stunninghttp://www.diyphotography.net/5-tips-creating-perfect-cinematic-lighting-making-work-look-stunning/ 1. Learn the rules of lighting 2. Learn when to break the rules 3. Make your key light larger 4. Reverse keying 5. Always be backlighting 
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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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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome BallRead 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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Outpost VFX lighting tipsRead more: Outpost VFX lighting tipswww.outpost-vfx.com/en/news/18-pro-tips-and-tricks-for-lighting Get as much information regarding your plate lighting as possible - Always use a reference
- Replicate what is happening in real life
- Invest into a solid HDRI
- Start Simple
- Observe real world lighting, photography and cinematography
- Don’t neglect the theory
- Learn the difference between realism and photo-realism.
- Keep your scenes organised
  
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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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ICLight – Krea and ComfyUI light editingRead more: ICLight – Krea and ComfyUI light editinghttps://drive.google.com/drive/folders/16Aq1mqZKP-h8vApaN4FX5at3acidqPUv https://github.com/lllyasviel/IC-Light https://generativematte.blogspot.com/2025/03/comfyui-ic-light-relighting-exploration.html  Workflow Local copy  
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HDRI ResourcesRead more: HDRI ResourcesText2Light - https://www.cgtrader.com/free-3d-models/exterior/other/10-free-hdr-panoramas-created-with-text2light-zero-shot
- https://frozenburning.github.io/projects/text2light/
- https://github.com/FrozenBurning/Text2Light
 Royalty free links - https://locationtextures.com/panoramas/
- http://www.noahwitchell.com/freebies
- https://polyhaven.com/hdris
- https://hdrmaps.com/
- https://www.ihdri.com/
- https://hdrihaven.com/
- https://www.domeble.com/
- http://www.hdrlabs.com/sibl/archive.html
- https://www.hdri-hub.com/hdrishop/hdri
- http://noemotionhdrs.net/hdrevening.html
- https://www.openfootage.net/hdri-panorama/
- https://www.zwischendrin.com/en/browse/hdri
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