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LATEST POSTS
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Marigold – repurposing diffusion-based image generators for dense predictions
Marigold repurposes Stable Diffusion for dense prediction tasks such as monocular depth estimation and surface normal prediction, delivering a level of detail often missing even in top discriminative models.
Key aspects that make it great:
– Reuses the original VAE and only lightly fine-tunes the denoising UNet
– Trained on just tens of thousands of synthetic image–modality pairs
– Runs on a single consumer GPU (e.g., RTX 4090)
– Zero-shot generalization to real-world, in-the-wild imageshttps://mlhonk.substack.com/p/31-marigold
https://arxiv.org/pdf/2505.09358
https://marigoldmonodepth.github.io/
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Runway Aleph
https://runwayml.com/research/introducing-runway-aleph
Generate New Camera Angles
Generate the Next Shot
Use Any Style to Transfer to a Video
Change Environments, Locations, Seasons and Time of Day
Add Things to a Scene
Remove Things from a Scene
Change Objects in a Scene
Apply the Motion of a Video to an Image
Alter a Character’s Appearance
Recolor Elements of a Scene
Relight Shots
Green Screen Any Object, Person or Situation
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Mike Wong – AtoMeow – A Blue noise image stippling in Processing
https://github.com/mwkm/atoMeow
https://www.shadertoy.com/view/7s3XzX
This demo is created for coders who are familiar with this awesome creative coding platform. You may quickly modify the code to work for video or to stipple your own Procssing drawings by turning them into
PImageand run the simulation. This demo code also serves as a reference implementation of my article Blue noise sampling using an N-body simulation-based method. If you are interested in 2.5D, you may mod the code to achieve what I discussed in this artist friendly article.Convert your video to a dotted noise.
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Aitor Echeveste – Free CG and Comp Projection Shot, Download the Assets & Follow the Workflow
What’s Included:
- Cleaned and extended base plates
- Full Maya and Nuke 3D projection layouts
- Bullet and environment CG renders with AOVs (RGB, normals, position, ID, etc.)
- Explosion FX in slow motion
- 3D scene geometry for projection
- Camera + lensing setup
- Light groups and passes for look development
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Tauseef Fayyaz About readable code – Clean Code Practices
𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝗖𝗹𝗲𝗮𝗻 𝗖𝗼𝗱𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀:
🔹 Code Readability & Simplicity – Use meaningful names, write short functions, follow SRP, flatten logic, and remove dead code.
→ Clarity is a feature.
🔹 Function & Class Design – Limit parameters, favor pure functions, small classes, and composition over inheritance.
→ Structure drives scalability.
🔹 Testing & Maintainability – Write readable unit tests, avoid over-mocking, test edge cases, and refactor with confidence.
→ Test what matters.
🔹 Code Structure & Architecture – Organize by features, minimize global state, avoid god objects, and abstract smartly.
→ Architecture isn’t just backend.
🔹 Refactoring & Iteration – Apply the Boy Scout Rule, DRY, KISS, and YAGNI principles regularly.
→ Refactor like it’s part of development.
🔹 Robustness & Safety – Validate early, handle errors gracefully, avoid magic numbers, and favor immutability.
→ Safe code is future-proof.
🔹 Documentation & Comments – Let your code explain itself. Comment why, not what, and document at the source.
→ Good docs reduce team friction.
🔹 Tooling & Automation – Use linters, formatters, static analysis, and CI reviews to automate code quality.
→ Let tools guard your gates.
🔹 Final Review Practices – Review, refactor nearby code, and avoid cleverness in the name of brevity.
→ Readable code is better than smart code. -
Mark Theriault “Steamboat Willie” – AI Re-Imagining of a 1928 Classic in 4k
I ran Steamboat Willie (now public domain) through Flux Kontext to reimagine it as a 3D-style animated piece. Instead of going the polished route with something like W.A.N. 2.1 for full image-to-video generation, I leaned into the raw, handmade vibe that comes from converting each frame individually. It gave it a kind of stop-motion texture, imperfect, a bit wobbly, but full of character.
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Microsoft DAViD – Data-efficient and Accurate Vision Models from Synthetic Data
Our human-centric dense prediction model delivers high-quality, detailed (depth) results while achieving remarkable efficiency, running orders of magnitude faster than competing methods, with inference speeds as low as 21 milliseconds per frame (the large multi-task model on an NVIDIA A100). It reliably captures a wide range of human characteristics under diverse lighting conditions, preserving fine-grained details such as hair strands and subtle facial features. This demonstrates the model’s robustness and accuracy in complex, real-world scenarios.
https://microsoft.github.io/DAViD
The state of the art in human-centric computer vision achieves high accuracy and robustness across a diverse range of tasks. The most effective models in this domain have billions of parameters, thus requiring extremely large datasets, expensive training regimes, and compute-intensive inference. In this paper, we demonstrate that it is possible to train models on much smaller but high-fidelity synthetic datasets, with no loss in accuracy and higher efficiency. Using synthetic training data provides us with excellent levels of detail and perfect labels, while providing strong guarantees for data provenance, usage rights, and user consent. Procedural data synthesis also provides us with explicit control on data diversity, that we can use to address unfairness in the models we train. Extensive quantitative assessment on real input images demonstrates accuracy of our models on three dense prediction tasks: depth estimation, surface normal estimation, and soft foreground segmentation. Our models require only a fraction of the cost of training and inference when compared with foundational models of similar accuracy.
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Embedding frame ranges into Quicktime movies with FFmpeg
QuickTime (.mov) files are fundamentally time-based, not frame-based, and so don’t have a built-in, uniform “first frame/last frame” field you can set as numeric frame IDs. Instead, tools like Shotgun Create rely on the timecode track and the movie’s duration to infer frame numbers. If you want Shotgun to pick up a non-default frame range (e.g. start at 1001, end at 1064), you must bake in an SMPTE timecode that corresponds to your desired start frame, and ensure the movie’s duration matches your clip length.
How Shotgun Reads Frame Ranges
- Default start frame is 1. If no timecode metadata is present, Shotgun assumes the movie begins at frame 1.
- Timecode ⇒ frame number. Shotgun Create “honors the timecodes of media sources,” mapping the embedded TC to frame IDs. For example, a 24 fps QuickTime tagged with a start timecode of 00:00:41:17 will be interpreted as beginning on frame 1001 (1001 ÷ 24 fps ≈ 41.71 s).
Embedding a Start Timecode
QuickTime uses a
tmcd(timecode) track. You can bake in an SMPTE track via FFmpeg’s-timecodeflag or via Compressor/encoder settings:- Compute your start TC.
- Desired start frame = 1001
- Frame 1001 at 24 fps ⇒ 1001 ÷ 24 ≈ 41.708 s ⇒ TC 00:00:41:17
- FFmpeg example:
ffmpeg -i input.mov \ -c copy \ -timecode 00:00:41:17 \ output.movThis adds a timecode track beginning at 00:00:41:17, which Shotgun maps to frame 1001.
Ensuring the Correct End Frame
Shotgun infers the last frame from the movie’s duration. To end on frame 1064:
- Frame count = 1064 – 1001 + 1 = 64 frames
- Duration = 64 ÷ 24 fps ≈ 2.667 s
FFmpeg trim example:
ffmpeg -i input.mov \ -c copy \ -timecode 00:00:41:17 \ -t 00:00:02.667 \ output_trimmed.movThis results in a 64-frame clip (1001→1064) at 24 fps.
FEATURED POSTS
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9 Best Hacks to Make a Cinematic Video with Any Camera
https://www.flexclip.com/learn/cinematic-video.html
- Frame Your Shots to Create Depth
- Create Shallow Depth of Field
- Avoid Shaky Footage and Use Flexible Camera Movements
- Properly Use Slow Motion
- Use Cinematic Lighting Techniques
- Apply Color Grading
- Use Cinematic Music and SFX
- Add Cinematic Fonts and Text Effects
- Create the Cinematic Bar at the Top and the Bottom
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Google’s Nano Banana AI: Free Tool for 3D Architecture Models
https://landscapearchitecture.store/blogs/news/nano-banana-ai-free-tool-for-3d-architecture-models
How to Use Nano Banana AI for Architecture- Go to Google AI Studio.
- Log in with your Gmail and select Gemini 2.5 (Nano Banana).
- Upload a photo — either from your laptop or a Google Street View screenshot.
- Paste this example prompt:
“Use the provided architectural photo as reference. Generate a high-fidelity 3D building model in the look of a 3D-printed architecture model.” - Wait a few seconds, and your 3D architecture model will be ready.
Pro tip: If you want more accuracy, upload two images — a street photo for the facade and an aerial view for the roof/top.
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What the Boeing 737 MAX’s crashes can teach us about production business – the effects of commoditisation
Airplane manufacturing is no different from mortgage lending or insulin distribution or make-believe blood analyzing software (or VFX?) —another cash cow for the one percent, bound inexorably for the slaughterhouse.
The beginning of the end was “Boeing’s 1997 acquisition of McDonnell Douglas, a dysfunctional firm with a dilapidated aircraft plant in Long Beach and a CEO (Harry Stonecipher) who liked to use what he called the “Hollywood model” for dealing with engineers: Hire them for a few months when project deadlines are nigh, fire them when you need to make numbers.” And all that came with it. “Stonecipher’s team had driven the last nail in the coffin of McDonnell’s flailing commercial jet business by trying to outsource everything but design, final assembly, and flight testing and sales.”
It is understood, now more than ever, that capitalism does half-assed things like that, especially in concert with computer software and oblivious regulators.
There was something unsettlingly familiar when the world first learned of MCAS in November, about two weeks after the system’s unthinkable stupidity drove the two-month-old plane and all 189 people on it to a horrific death. It smacked of the sort of screwup a 23-year-old intern might have made—and indeed, much of the software on the MAX had been engineered by recent grads of Indian software-coding academies making as little as $9 an hour, part of Boeing management’s endless war on the unions that once represented more than half its employees.
Down in South Carolina, a nonunion Boeing assembly line that opened in 2011 had for years churned out scores of whistle-blower complaints and wrongful termination lawsuits packed with scenes wherein quality-control documents were regularly forged, employees who enforced standards were sabotaged, and planes were routinely delivered to airlines with loose screws, scratched windows, and random debris everywhere.
Shockingly, another piece of the quality failure is Boeing securing investments from all airliners, starting with SouthWest above all, to guarantee Boeing’s production lines support in exchange for fair market prices and favorite treatments. Basically giving Boeing financial stability independently on the quality of their product. “Those partnerships were but one numbers-smoothing mechanism in a diversified tool kit Boeing had assembled over the previous generation for making its complex and volatile business more palatable to Wall Street.”
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Custom bokeh in a raytraced DOF render
To achieve a custom pinhole camera effect with a custom bokeh in Arnold Raytracer, you can follow these steps:
- Set the render camera with a focal length around 50 (or as needed)
- Set the F-Stop to a high value (e.g., 22).
- Set the focus distance as you require
- Turn on DOF
- Place a plane a few cm in front of the camera.
- Texture the plane with a transparent shape at the center of it. (Transmission with no specular roughness)