COMPOSITION
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Cinematographers Blueprint 300dpi posterRead more: Cinematographers Blueprint 300dpi posterThe 300dpi digital poster is now available to all PixelSham.com subscribers. If you have already subscribed and wish a copy, please send me a note through the contact page. 
DESIGN
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Kristina Kashtanova – “This is how GPT-4 sees and hears itself”Read more: Kristina Kashtanova – “This is how GPT-4 sees and hears itself”“I used GPT-4 to describe itself. Then I used its description to generate an image, a video based on this image and a soundtrack. Tools I used: GPT-4, Midjourney, Kaiber AI, Mubert, RunwayML This is the description I used that GPT-4 had of itself as a prompt to text-to-image, image-to-video, and text-to-music. I put the video and sound together in RunwayML. GPT-4 described itself as: “Imagine a sleek, metallic sphere with a smooth surface, representing the vast knowledge contained within the model. The sphere emits a soft, pulsating glow that shifts between various colors, symbolizing the dynamic nature of the AI as it processes information and generates responses. The sphere appears to float in a digital environment, surrounded by streams of data and code, reflecting the complex algorithms and computing power behind the AI” 
COLOR
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Photography Basics : Spectral Sensitivity Estimation Without a CameraRead more: Photography Basics : Spectral Sensitivity Estimation Without a Camerahttps://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/ A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession. To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space. Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.  
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The Color of Infinite TemperatureRead more: The Color of Infinite TemperatureThis is the color of something infinitely hot.  Of course you’d instantly be fried by gamma rays of arbitrarily high frequency, but this would be its spectrum in the visible range. johncarlosbaez.wordpress.com/2022/01/16/the-color-of-infinite-temperature/ This is also the color of a typical neutron star. They’re so hot they look the same. 
 It’s also the color of the early Universe!This was worked out by David Madore.  The color he got is sRGB(148,177,255). 
 www.htmlcsscolor.com/hex/94B1FFAnd according to the experts who sip latte all day and make up names for colors, this color is called ‘Perano’. 
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PBR Color Reference List for Materials – by Grzegorz BaranRead more: PBR Color Reference List for Materials – by Grzegorz Baran“The list should be helpful for every material artist who work on PBR materials as it contains over 200 color values measured with PCE-RGB2 1002 Color Spectrometer device and presented in linear and sRGB (2.2) gamma space. All color values, HUE and Saturation in this list come from measurements taken with PCE-RGB2 1002 Color Spectrometer device and are presented in linear and sRGB (2.2) gamma space (more info at the end of this video) I calculated Relative Luminance and Luminance values based on captured color using my own equation which takes color based luminance perception into consideration. Bare in mind that there is no ‘one’ color per substance as nothing in nature is even 100% uniform and any value in +/-10% range from these should be considered as correct one. Therefore this list should be always considered as a color reference for material’s albedos, not ulitimate and absolute truth.“ 
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StudioBinder.com – CRI color rendering indexRead 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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About color: What is a LUTRead more: About color: What is a LUThttp://www.lightillusion.com/luts.html https://www.shutterstock.com/blog/how-use-luts-color-grading A LUT (Lookup Table) is essentially the modifier between two images, the original image and the displayed image, based on a mathematical formula. Basically conversion matrices of different complexities. There are different types of LUTS – viewing, transform, calibration, 1D and 3D. 
LIGHTING
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Convert between light exposure and intensityRead 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)Why Exposure?Exposure is a stop value that multiplies the intensity by 2 to the power of the stop. Increasing exposure by 1 results in double the amount of light. 
 Artists think in “stops.” Doubling or halving brightness is easy math and common in grading and look-dev.
 Exposure counts doublings in whole stops:- +1 stop = ×2 brightness
- −1 stop = ×0.5 brightness
 This gives perceptually even controls across both bright and dark values. 
 Why Intensity?Intensity is linear. 
 It’s what render engines and compositors expect when:- Summing values
- Averaging pixels
- Multiplying or filtering pixel data
 Use intensity when you need the actual math on pixel/light data. 
 Formulas (from your Python)- Intensity from exposure: intensity = 2**exposure
- Exposure from intensity: exposure = log₂(intensity)
 Guardrails: - Intensity must be > 0 to compute exposure.
- If intensity = 0 → exposure is undefined.
- Clamp tiny values (e.g. 1e−323) before using log₂.
 
 Use Exposure (stops) when…- You want artist-friendly sliders (−5…+5 stops)
- Adjusting look-dev or grading in even stops
- Matching plates with quick ±1 stop tweaks
- Tweening brightness changes smoothly across ranges
 
 Use Intensity (linear) when…- Storing raw pixel/light values
- Multiplying textures or lights by a gain
- Performing sums, averages, and filters
- Feeding values to render engines expecting linear data
 
 Examples- +2 stops → 2**2 = 4.0 (×4)
- +1 stop → 2**1 = 2.0 (×2)
- 0 stop → 2**0 = 1.0 (×1)
- −1 stop → 2**(−1) = 0.5 (×0.5)
- −2 stops → 2**(−2) = 0.25 (×0.25)
- Intensity 0.1 → exposure = log₂(0.1) ≈ −3.32
 
 Rule of thumbThink in stops (exposure) for controls and matching. 
 Compute in linear (intensity) for rendering and math.
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StudioBinder.com – CRI color rendering indexRead 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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Romain Chauliac – LightIt a lighting script for Maya and ArnoldRead more: Romain Chauliac – LightIt a lighting script for Maya and ArnoldLightIt is a script for Maya and Arnold that will help you and improve your lighting workflow. 
 Thanks to preset studio lighting components (lights, backdrop…), high quality studio scenes and HDRI library manager.https://www.artstation.com/artwork/393emJ 
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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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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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