https://prodpro.com/blog/q2-2024-global-production-report/

What’s important: Over the past 6 months, the total number of productions filming globally in 2024 is still 16% lower than in 2022, and 37% lower in the US.

Why it matters: The lower volumes are here to stay.

https://playcanvas.com/supersplat/editor?load=https://raw.githubusercontent.com/willeastcott/assets/main/iceberg.ply&camera.debug=false&show.bound=false&show.grid=false

https://github.com/david-rhodes/GSOPs

https://www.linkedin.com/posts/davidarhodes_gsops-gaussiansplatting-3dgs-activity-7250970758321975296-k7f7

https://player.style/

https://peterszasz.com/engineering-managers-guide-to-effective-annual-feedback

The main goals of a regular, written feedback cycle are:

• Recognition, support for self-reflection and personal growth
• Alignment with team- and company needs
• Documentation

These promote:

• Recognize Achievements: Use the feedback process to boost morale and support self-reflection.
• Align Goals: Ensure individual contributions match company objectives.
• Document Progress: Keep a clear record of performance for future decisions.
• Prepare Feedback: Gather 360-degree feedback, focus on examples, and anticipate reactions.
• Strength-Based Approach: Focus on enhancing strengths over fixing weaknesses.
• Deliver Feedback Live: Engage in discussion before providing written feedback.
• Follow-Up: Use feedback to guide future goals and performance improvement.

GIL or Global Interpreter Lock can be disabled in Python version 3.13. This is currently experimental.

What is GIL? It is a mechanism used by the CPython interpreter to ensure that only one thread executes the Python bytecode at a time.

https://medium.com/@r_bilan/python-3-13-without-the-gil-a-game-changer-for-concurrency-5e035500f0da

Advantages of the GIL

1. Simplicity of Implementation: The GIL simplifies memory management in CPython by preventing concurrent access to Python objects, which can help avoid race conditions and other threading issues.
2. Ease of Use for Single-Threaded Programs: For applications that are single-threaded, the GIL eliminates the overhead associated with managing thread safety, allowing for straightforward and efficient code execution.
3. Compatibility with C Extensions: The GIL allows C extensions to operate without needing to implement complex threading models, which simplifies the development of Python extensions that interface with C libraries.
4. Performance for I/O-Bound Tasks: In I/O-bound applications, the GIL does not significantly hinder performance since threads can be switched out during I/O operations, allowing other threads to run.

Disadvantages of the GIL

1. Limited Multithreading Performance: The GIL can severely restrict the performance of CPU-bound multithreaded applications, as it only allows one thread to execute Python bytecode at a time, leading to underutilization of multicore processors.
2. Thread Management Complexity: Although the GIL simplifies memory management, it can complicate the design of concurrent applications, forcing developers to carefully manage threading issues or use multiprocessing instead.
3. Hindrance to Parallel Processing: With the GIL enabled, achieving true parallelism in Python applications is challenging, making it difficult for developers to leverage multicore architectures effectively.
4. Inefficiency in Context Switching: Frequent context switching due to the GIL can introduce overhead, especially in applications with many threads, leading to performance degradation.

https://geekpython.in/gil-become-optional-in-python

https://www.linkedin.com/posts/bengunsberger_ai-podcasts-notebooklm-activity-7247492413705871361-JVnM

https://notebooklm.google/

Listen to the podcast in the post

“I just created a AI-Generated podcast by feeding an article I write into Google’s NotebookLM. If I hadn’t make it myself, I would have been 100% fooled into thinking it was real people talking.”

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https://venturebeat.com/ai/apple-releases-depth-pro-an-ai-model-that-rewrites-the-rules-of-3d-vision/

The model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.

https://github.com/apple/ml-depth-pro

https://arxiv.org/pdf/2410.02073

https://www.prime1studio.com/

https://www.prime1studio.com/navigate?cgid=series

https://ai.meta.com/research/movie-gen/

https://devpedia.pages.dev/

https://www.colour-science.org/anders-langlands/

This page compares images rendered in Arnold using spectral rendering and different sets of colourspace primaries: Rec.709, Rec.2020, ACES and DCI-P3. The SPD data for the GretagMacbeth Color Checker are the measurements of Noburu Ohta, taken from Mansencal, Mauderer and Parsons (2014) colour-science.org.

https://bottosson.github.io/posts/colorwrong/

Most software around us today are decent at accurately displaying colors. Processing of colors is another story unfortunately, and is often done badly.

To understand what the problem is, let’s start with an example of three ways of blending green and magenta:

• Perceptual blend – A smooth transition using a model designed to mimic human perception of color. The blending is done so that the perceived brightness and color varies smoothly and evenly.
• Linear blend – A model for blending color based on how light behaves physically. This type of blending can occur in many ways naturally, for example when colors are blended together by focus blur in a camera or when viewing a pattern of two colors at a distance.
• sRGB blend – This is how colors would normally be blended in computer software, using sRGB to represent the colors. 

Let’s look at some more examples of blending of colors, to see how these problems surface more practically. The examples use strong colors since then the differences are more pronounced. This is using the same three ways of blending colors as the first example.

Instead of making it as easy as possible to work with color, most software make it unnecessarily hard, by doing image processing with representations not designed for it. Approximating the physical behavior of light with linear RGB models is one easy thing to do, but more work is needed to create image representations tailored for image processing and human perception.

Also see:

https://www.pixelsham.com/2022/04/05/bjorn-ottosson-okhsv-and-okhsl-two-new-color-spaces-for-color-picking/

https://radiancefields.com/how-ever-(exact-volumetric-ellipsoid-rendering)-does-this-work

https://half-potato.gitlab.io/posts/ever/

Unlike previous methods like Gaussian Splatting, EVER leverages ellipsoids instead of Gaussians and uses Ray Tracing instead of Rasterization. This shift eliminates artifacts like popping and blending inconsistencies, offering sharper and more accurate renderings.

Best alternatives to Adobe:

https://github.com/KenneyNL/Adobe-Alternatives

• Affinity (Photo and illustration editing) https://affinity.serif.com/
• DaVinci Resolve (video editing): https://www.blackmagicdesign.com/au/products/davinciresolve/
• Clip Studio Paint (illustration): https://www.clipstudio.net/en/
• Toon Boom (animation): https://www.toonboom.com/

https://www.theverge.com/2024/10/1/24259369/microsoft-hololens-2-discontinuation-support

Software support for the original HoloLens headset will end on December 10th.

Microsoft’s struggles with HoloLens have been apparent over the past two years.

https://www.vfxatlas.com/

Local copy in the post

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Bonus clip in the post: Character Design Concept Art Process – Professional Workflow

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𝐌𝐞𝐭𝐚 𝐇𝐲𝐩𝐞𝐫𝐬𝐜𝐚𝐩𝐞 𝐢𝐧 𝐚 𝐧𝐮𝐭𝐬𝐡𝐞𝐥𝐥
Hyperscape technology allows us to scan spaces with just a phone and create photorealistic replicas of the physical world with high fidelity. You can experience these digital replicas on the Quest 3 or on the just announced Quest 3S.

https://www.youtube.com/clip/UgkxGlXM3v93kLg1D9qjJIKmvIYW-vHvdbd0

𝐇𝐢𝐠𝐡 𝐅𝐢𝐝𝐞𝐥𝐢𝐭𝐲 𝐄𝐧𝐚𝐛𝐥𝐞𝐬 𝐚 𝐍𝐞𝐰 𝐒𝐞𝐧𝐬𝐞 𝐨𝐟 𝐏𝐫𝐞𝐬𝐞𝐧𝐜𝐞
This level of photorealism will enable a new way to be together, where spaces look, sound, and feel like you are physically there.

𝐒𝐢𝐦𝐩𝐥𝐞 𝐂𝐚𝐩𝐭𝐮𝐫𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐦𝐨𝐛𝐢𝐥𝐞 𝐩𝐡𝐨𝐧𝐞
Currently not available, but in the future, it will offer a new way to create worlds in Horizon and will be the easiest way to bring physical spaces to the digital world. Creators can capture physical environments on their mobile device and invite friends, fans, or customers to visit and engage in the digital replicas.

𝐂𝐥𝐨𝐮𝐝-𝐛𝐚𝐬𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐑𝐞𝐧𝐝𝐞𝐫𝐢𝐧𝐠
Using Gaussian Splatting, a 3D modeling technique that renders fine details with high accuracy and efficiency, we process the model input data in the cloud and render the created model through cloud rendering and streaming on Quest 3 and the just announced Quest 3S.

𝐓𝐫𝐲 𝐢𝐭 𝐨𝐮𝐭 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟
If you are in the US and you have a Meta Quest 3 or 3S you can try it out here:

https://www.meta.com/experiences/meta-horizon-hyperscape-demo/7972066712871980/

https://www.yankodesign.com/2024/09/18/principles-of-interior-design-balance

The three types of balance include:

• Symmetrical Balance
• Asymmetrical Balance
• Radial Balance

https://www.reasonable.work/colors/

https://archive.org/details/the-art-of-nimona_202310/mode/2up

Local compressed copy in the post

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