BREAKING NEWS
LATEST POSTS
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Finn Jager – From HEIC (High Efficiency Image Container) iPhone to a Multichannel EXR
Finn Jäger has spent some time in making a sleeker tool for all you VFX nerds out there, it takes a HEIC iPhone still and exports a Multichannel EXR – the cool thing is it also converts it to acesCG and it merges the SDR base image with the gain map according to apples math hdr_rgb = sdr_rgb * (1.0 + (headroom – 1.0) * gainmap)
https://github.com/finnschi/heic-shenanigans
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Mars Lewis on the Brandolini’s Law
Brandolini’s law (or the bullshit asymmetry principle) is an internet adage coined in 2013 by Italian programmer Alberto Brandolini. It compares the considerable effort of debunking misinformation to the relative ease of creating it in the first place.
The law states: “The amount of energy needed to refute bullshit is an order of magnitude bigger than to produce it.”
https://en.wikipedia.org/wiki/Brandolini%27s_law
This is why every time you kill a lie, it feels like nothing changed. It’s why no matter how many facts you post, how many sources you cite, how many receipts you show—the swarm just keeps coming. Because while you’re out in the open doing surgery, the machine is behind the curtain spraying aerosol deceit into every vent.
The lie takes ten seconds. The truth takes ten paragraphs. And by the time you’ve written the tenth, the people you’re trying to reach have already scrolled past.
Every viral deception—the fake quote, the rigged video, the synthetic outrage—takes almost nothing to create. And once it’s out there, you’re not just correcting a fact—you’re prying it out of someone’s identity. Because people don’t adopt lies just for information. They adopt them for belonging. The lie becomes part of who they are, and your correction becomes an attack.
And still—you must correct it. Still, you must fight.
Because even if truth doesn’t spread as fast, it roots deeper. Even if it doesn’t go viral, it endures. And eventually, it makes people bulletproof to the next wave of narrative sewage.
You’re not here to win a one-day war. You’re here to outlast a never-ending invasion.
The lies are roaches. You kill one, and a hundred more scramble behind the drywall.The lies are Hydra heads. You cut one off, and two grow back. But you keep swinging anyway.
Because this isn’t about instant wins. It’s about making the cost of lying higher. It’s about being the resistance that doesn’t fold. You don’t fight because it’s easy. You fight because it’s right.
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GenUE – Direct Prompt-to-Mesh Generation in Unreal Engine Integrated with ComfyUI
GenUE brings prompt-driven 3D asset creation directly into Unreal Engine using ComfyUI as a flexible backend. • Generate high-quality images from text prompts. • Choose from a catalog of batch-generated images – no style limitations. • Convert the selected image to a fully textured 3D mesh. • Automatically import and place the model into your Unreal Engine scene. This modular pipeline gives you full control over the image and 3D generation stages, with support for any ComfyUI workflow or model. Full generation (image + mesh + import) completes in under 2 minutes on a high-end consumer GPU.
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Edward Ureña – Rig creator
https://edwardurena.gumroad.com/l/ramoo
What it offers:
• Base rigs for multiple character types
• Automatic weight application
• Built-in facial rigging system
• Bone generators with FK and IK options
• Streamlined constraint panel
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GPT-Image-1 API now available through ComfyUI with Dall-E integration
https://blog.comfy.org/p/comfyui-now-supports-gpt-image-1
https://docs.comfy.org/tutorials/api-nodes/openai/gpt-image-1
https://openai.com/index/image-generation-api
• Prompt GPT-Image-1 directly in ComfyUI using text or image inputs
• Set resolution and quality
• Supports image editing + transparent backgrounds
• Seamlessly mix with local workflows like WAN 2.1, FLUX Tools, and more
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Tencent Hunyuan3D 2.5 – Transform images and text into 3D models with ultra-high-definition precision
What makes it special?
• Massive 10B parameter geometric model with 10x more mesh faces.
• High-quality textures with industry-first multi-view PBR generation.
• Optimized skeletal rigging for streamlined animation workflows.
• Flexible pipeline for text-to-3D and image-to-3D generation.
They’re making it accessible to everyone:
• Open-source code and pre-trained models.
• Easy-to-use API and intuitive web interface.
• Free daily quota doubled to 20 generations!
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Alibaba 3DV-TON – A novel diffusion model for HQ and temporally consistent video
https://arxiv.org/pdf/2504.17414
Video try-on replaces clothing in videos with target garments. Existing methods struggle to generate high-quality and temporally consistent results when handling complex clothing patterns and diverse body poses. We present 3DV-TON, a novel diffusion-based framework for generating high-fidelity and temporally consistent video try-on results. Our approach employs generated animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. This is achieved by enabling direct reference to consistent garment texture movements throughout video sequences. The proposed method features an adaptive pipeline for generating dynamic 3D guidance: (1) selecting a keyframe for initial 2D image try-on, followed by (2) reconstructing and animating a textured 3D mesh synchronized with original video poses. We further introduce a robust rectangular masking strategy that successfully mitigates artifact propagation caused by leaking clothing information during dynamic human and garment movements. To advance video try-on research, we introduce HR-VVT, a high-resolution benchmark dataset containing 130 videos with diverse clothing types and scenarios. Quantitative and qualitative results demonstrate our superior performance over existing methods.
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FramePack – Packing Input Frame Context in Next-Frame Prediction Models for Offline Video Generation With Low Resource Requirements
https://lllyasviel.github.io/frame_pack_gitpage/
- Diffuse thousands of frames at full fps-30 with 13B models using 6GB laptop GPU memory.
- Finetune 13B video model at batch size 64 on a single 8xA100/H100 node for personal/lab experiments.
- Personal RTX 4090 generates at speed 2.5 seconds/frame (unoptimized) or 1.5 seconds/frame (teacache).
- No timestep distillation.
- Video diffusion, but feels like image diffusion.
Image-to-5-Seconds (30fps, 150 frames)
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Anthony Sauzet – ProceduralMaya
A Maya script that introduces a node-based graph system for procedural modeling, like Houdini
https://github.com/AnthonySTZ/ProceduralMaya
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11 Public Speaking Strategies
What do people report as their #1 greatest fear?
It’s not death….
It’s public speaking.
Glossophobia, the fear of public speaking, has been a daunting obstacle for me for years.
11 confidence-boosting tips
1/ The 5-5-5 Rule
→ Scan 5 faces; Hold each gaze for 5 seconds.
→ Repeat every 5 minutes.
→ Creates an authentic connection.
2/Power Pause
→ Dead silence for 3 seconds after key points.
→ Let your message land.
3/ The 3-Part Open
→ Hook with a question.
→ Share a story.
→ State your promise.
4/ Palm-Up Principle
→ Open palms when speaking = trustworthy.
→ Pointing fingers = confrontational.
5/ The 90-Second Reset
→ Feel nervous? Excuse yourself.
→ 90 seconds of deep breathing reset your nervous system.
6/ Rule of Three
→ Structure key points in threes.
→ Our brains love patterns.
7/ 2-Minute Story Rule
→ Keep stories under 2 minutes.
→ Any longer, you lose them.
8/ The Lighthouse Method
→ Plant “anchor points” around the room.
→ Rotate eye contact between them.
→ Looks natural, feels structured.
9/ The Power Position
→ Feet shoulder-width apart.
→ Hands relaxed at sides.
→ Projects confidence even when nervous.
10/ The Callback Technique
→ Reference earlier points later in your talk.
→ Creates a narrative thread.
→ Audiences love connections.
11/ The Rehearsal Truth
→ Practice the opening 3x more than the rest.
→ Nail the first 30 seconds; you’ll nail the talk.
FEATURED POSTS
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GretagMacbeth Color Checker Numeric Values and Middle Gray
The human eye perceives half scene brightness not as the linear 50% of the present energy (linear nature values) but as 18% of the overall brightness. We are biased to perceive more information in the dark and contrast areas. A Macbeth chart helps with calibrating back into a photographic capture into this “human perspective” of the world.
https://en.wikipedia.org/wiki/Middle_gray
In photography, painting, and other visual arts, middle gray or middle grey is a tone that is perceptually about halfway between black and white on a lightness scale in photography and printing, it is typically defined as 18% reflectance in visible light
Light meters, cameras, and pictures are often calibrated using an 18% gray card[4][5][6] or a color reference card such as a ColorChecker. On the assumption that 18% is similar to the average reflectance of a scene, a grey card can be used to estimate the required exposure of the film.
https://en.wikipedia.org/wiki/ColorChecker
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AI Data Laundering: How Academic and Nonprofit Researchers Shield Tech Companies from Accountability
“Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.”
“In addition to the Shutterstock clips, Meta also used 10 million video clips from this 100M video dataset from Microsoft Research Asia. It’s not mentioned on their GitHub, but if you dig into the paper, you learn that every clip came from over 3 million YouTube videos.”
“It’s become standard practice for technology companies working with AI to commercially use datasets and models collected and trained by non-commercial research entities like universities or non-profits.”
“Like with the artists, photographers, and other creators found in the 2.3 billion images that trained Stable Diffusion, I can’t help but wonder how the creators of those 3 million YouTube videos feel about Meta using their work to train their new model.”
