COMPOSITION
DESIGN
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How to paint a boardgame miniaturesRead more: How to paint a boardgame miniaturesSteps: - soap wash cleaning
- primer
- base-coat layer (black/white)
- detailing
- washing aka shade (could be done after highlighting)
- highlights aka dry brushing (could be done after washing)
- varnish (gloss/satin/matte)
 
COLOR
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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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Gamma correctionRead more: Gamma correction http://www.normankoren.com/makingfineprints1A.html#Gammabox https://en.wikipedia.org/wiki/Gamma_correction http://www.photoscientia.co.uk/Gamma.htm https://www.w3.org/Graphics/Color/sRGB.html http://www.eizoglobal.com/library/basics/lcd_display_gamma/index.html https://forum.reallusion.com/PrintTopic308094.aspx Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships. Three main types: 
 – Image Gamma encoded in images
 – Display Gammas encoded in hardware and/or viewing time
 – System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.
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FXGuide – ACES 2.0 with ILM’s Alex FryRead more: FXGuide – ACES 2.0 with ILM’s Alex Fryhttps://draftdocs.acescentral.com/background/whats-new/ ACES 2.0 is the second major release of the components that make up the ACES system. The most significant change is a new suite of rendering transforms whose design was informed by collected feedback and requests from users of ACES 1. The changes aim to improve the appearance of perceived artifacts and to complete previously unfinished components of the system, resulting in a more complete, robust, and consistent product. Highlights of the key changes in ACES 2.0 are as follows: - New output transforms, including:
- A less aggressive tone scale
- More intuitive controls to create custom outputs to non-standard displays
- Robust gamut mapping to improve perceptual uniformity
- Improved performance of the inverse transforms
 
- Enhanced AMF specification
- An updated specification for ACES Transform IDs
- OpenEXR compression recommendations
- Enhanced tools for generating Input Transforms and recommended procedures for characterizing prosumer cameras
- Look Transform Library
- Expanded documentation
 Rendering TransformThe most substantial change in ACES 2.0 is a complete redesign of the rendering transform. ACES 2.0 was built as a unified system, rather than through piecemeal additions. Different deliverable outputs “match” better and making outputs to display setups other than the provided presets is intended to be user-driven. The rendering transforms are less likely to produce undesirable artifacts “out of the box”, which means less time can be spent fixing problematic images and more time making pictures look the way you want. Key design goals- Improve consistency of tone scale and provide an easy to use parameter to allow for outputs between preset dynamic ranges
- Minimize hue skews across exposure range in a region of same hue
- Unify for structural consistency across transform type
- Easy to use parameters to create outputs other than the presets
- Robust gamut mapping to improve harsh clipping artifacts
- Fill extents of output code value cube (where appropriate and expected)
- Invertible – not necessarily reversible, but Output > ACES > Output round-trip should be possible
- Accomplish all of the above while maintaining an acceptable “out-of-the box” rendering
 
- New output transforms, including:
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Virtual Production volumes studyRead more: Virtual Production volumes studyColor Fidelity in LED Volumes 
 https://theasc.com/articles/color-fidelity-in-led-volumesVirtual Production Glossary 
 https://vpglossary.com/What is Virtual Production – In depth analysis 
 https://www.leadingledtech.com/what-is-a-led-virtual-production-studio-in-depth-technical-analysis/A comparison of LED panels for use in Virtual Production: 
 Findings and recommendations
 https://eprints.bournemouth.ac.uk/36826/1/LED_Comparison_White_Paper%281%29.pdf
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The 7 key elements of brand identity design + 10 corporate identity examplesRead more: The 7 key elements of brand identity design + 10 corporate identity exampleswww.lucidpress.com/blog/the-7-key-elements-of-brand-identity-design 1. Clear brand purpose and positioning 2. Thorough market research 3. Likable brand personality 4. Memorable logo 5. Attractive color palette 6. Professional typography 7. On-brand supporting graphics 
LIGHTING
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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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Fast, optimized ‘for’ pixel loops with OpenCV and Python to create tone mapped HDR imagesRead more: Fast, optimized ‘for’ pixel loops with OpenCV and Python to create tone mapped HDR imageshttps://pyimagesearch.com/2017/08/28/fast-optimized-for-pixel-loops-with-opencv-and-python/ https://learnopencv.com/exposure-fusion-using-opencv-cpp-python/ Exposure Fusion is a method for combining images taken with different exposure settings into one image that looks like a tone mapped High Dynamic Range (HDR) image. 
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studiobinder.com – What is Tenebrism and Hard Lighting — The Art of Light and Shadow and chiaroscuro ExplainedRead more: studiobinder.com – What is Tenebrism and Hard Lighting — The Art of Light and Shadow and chiaroscuro Explainedhttps://www.studiobinder.com/blog/what-is-tenebrism-art-definition/ https://www.studiobinder.com/blog/what-is-hard-light-photography/ 
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