This category is about Generative AI
If you’ve ever used a local Stable Diffusion setup, you know that long, descriptive prompts can sometimes slow down the sampling process. The research in this paper suggests that not every word in your prompt is actually “seen” by the U-Net during every step of the diffusion process. By pruning the least important tokens, we … Read more
We’ve all been there: you ask an LLM a question about a recent event or a specific technical paper, and it either hallucinates or admits its knowledge cutoff. That’s why the paper “Enhancing Large Language Models with Retrieval-Augmented Generation: A Comprehensive Overview” caught my eye. RAG isn’t just a “feature”—it’s a fundamental shift in how we build … Read more
One of the most frustrating limits of early Neural Radiance Fields (NeRF) was their “statue-like” nature. They were great for static objects, but as soon as something moved, the math broke. Recently, I’ve been obsessed with the paper “Unlocking Dynamic Scene Understanding: Neural Radiance Fields for Deformable Objects.” The premise is brilliant: instead of just mapping coordinates (x,y,z) to … Read more
There is a recurring “wall” every AI hobbyist hits when working with Novel View Synthesis (NVS). You generate a beautiful second view of a room, but as soon as you try to “walk” further into the scene, the geometry falls apart like a house of cards. Recently, I came across the paper “SceneCompleter: Dense 3D Scene … Read more