BREAKING NEWS
LATEST POSTS
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Skill Foundry – ARTIFICIAL INTELLIGENCE WITH PYTHON
INTRODUCTION………………………………………………………………………………………….. 3
Setting Up AI Development Environment with Python……………………………….… 7
Understanding Machine Learning — The Heart of AI…………………………………… 11
Supervised Learning Deep Dive — Regression and Classification Models………. 16
Unsupervised Learning Deep Dive — Discovering Hidden Patterns………………. 21
Neural Networks Fundamentals — Building Brains for AI ……………………………. 26
Project — Build a Neural Network to Classify Handwritten Digits ………………. 30
Deep Learning for Image Classification — CNNs Explained………………………… 33
Advanced Image Classification — Transfer Learning………………………………….. 37
Natural Language Processing (NLP) Basics with Python…………………………….. 41
Spam Detection Using Machine Learning …………………………………………………. 45
Deep Learning for Text Classification (with NLP) …………………………………….. 48
Computer Vision Basics and Image Classification ……………………………………. 51
AI for Automation: Files, Web, and Emails ………………………………………………. 56
AI Chatbots and Virtual Assistants …………………………………………………………… 61 -
Eyeline Labs VChain – Chain-of-Visual-Thought for Reasoning in Video Generation for better AI physics
https://eyeline-labs.github.io/VChain/
https://github.com/Eyeline-Labs/VChain
Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
FEATURED POSTS
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Pika.art – an AI for creating videos from stills
“It converts simple text instructions into captivating videos, in seconds.
The story behind this AI is fascinating: A team of four engineers, led by Demi Guo and Chenlin Meng, was born with a clear vision: to transform video creation.
After raising $55 million, Pika Labs initially focused on Japanese anime-style animations before expanding into 3D animation”
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Sun cone angle (angular diameter) as perceived by earth viewers
Also see:
https://www.pixelsham.com/2020/08/01/solid-angle-measures/
The cone angle of the sun refers to the angular diameter of the sun as observed from Earth, which is related to the apparent size of the sun in the sky.
The angular diameter of the sun, or the cone angle of the sunlight as perceived from Earth, is approximately 0.53 degrees on average. This value can vary slightly due to the elliptical nature of Earth’s orbit around the sun, but it generally stays within a narrow range.
Here’s a more precise breakdown:
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- Average Angular Diameter: About 0.53 degrees (31 arcminutes)
- Minimum Angular Diameter: Approximately 0.52 degrees (when Earth is at aphelion, the farthest point from the sun)
- Maximum Angular Diameter: Approximately 0.54 degrees (when Earth is at perihelion, the closest point to the sun)
This angular diameter remains relatively constant throughout the day because the sun’s distance from Earth does not change significantly over a single day.
To summarize, the cone angle of the sun’s light, or its angular diameter, is typically around 0.53 degrees, regardless of the time of day.
https://en.wikipedia.org/wiki/Angular_diameter
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AnimationXpress.com interviews Daniele Tosti for TheCgCareer.com channel
You’ve been in the VFX Industry for over a decade. Tell us about your journey.
It all started with my older brother giving me a Commodore64 personal computer as a gift back in the late 80′. I realised then I could create something directly from my imagination using this new digital media format. And, eventually, make a living in the process.
That led me to start my professional career in 1990. From live TV to games to animation. All the way to live action VFX in the recent years.I really never stopped to crave to create art since those early days. And I have been incredibly fortunate to work with really great talent along the way, which made my journey so much more effective.
What inspired you to pursue VFX as a career?
An incredible combination of opportunities, really. The opportunity to express myself as an artist and earn money in the process. The opportunity to learn about how the world around us works and how best solve problems. The opportunity to share my time with other talented people with similar passions. The opportunity to grow and adapt to new challenges. The opportunity to develop something that was never done before. A perfect storm of creativity that fed my continuous curiosity about life and genuinely drove my inspiration.
Tell us about the projects you’ve particularly enjoyed working on in your career
(more…)
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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
https://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.”
