COMPOSITION
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StudioBinder – Roger Deakins on How to Choose a Camera Lens — Cinematography Composition TechniquesRead more: StudioBinder – Roger Deakins on How to Choose a Camera Lens — Cinematography Composition Techniqueshttps://www.studiobinder.com/blog/camera-lens-buying-guide/ https://www.studiobinder.com/blog/e-books/camera-lenses-explained-volume-1-ebook 
DESIGN
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The illusion of sex 2009Read more: The illusion of sex 2009 Richard Russell Harvard University, USA In the Illusion of Sex, two faces are perceived as male and female. However, both faces are actually versions of the same androgynous face. One face was created by increasing the contrast of the androgynous face, while the other face was created by decreasing the contrast. The face with more contrast is perceived as female, while the face with less contrast is perceived as male. The Illusion of Sex demonstrates that contrast is an important cue for perceiving the sex of a face, with greater contrast appearing feminine, and lesser contrast appearing masculine. Russell, R. (2009) A sex difference in facial pigmentation and its exaggeration by cosmetics. Perception, (38)1211-1219. 
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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Capturing textures albedoRead 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. 
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Capturing the world in HDR for real time projects – Call of Duty: Advanced WarfareRead more: Capturing the world in HDR for real time projects – Call of Duty: Advanced WarfareReal-World Measurements for Call of Duty: Advanced Warfare www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf Local version Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf 
LIGHTING
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3D Lighting Tutorial by Amaan KramRead 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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Terminators and Iron Men: HDRI, Image-based lighting and physical shading at ILM – Siggraph 2010Read more: Terminators and Iron Men: HDRI, Image-based lighting and physical shading at ILM – Siggraph 2010
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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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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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