GANs, diffusion models, and the architectures behind modern generative AI. Research-grade explainers on how generative models learn, where they fail, and the design choices that separate state-of-the-art methods from the rest, each tied to the paper it came from.
Every hyperspectral sensor is fighting the same physics. To split light into dozens of narrow spectral bands the sensor has to gather less light per band, and less light per pixel means the imaging system trades spatial resolution for spectral…
Vector Quantized Priors for Sharper Hyperspectral image Fusion Read More »
Diffusion models and flow matching models have become the backbone of modern image and video generation, powering everything from text to image systems to text to 3D creation tools. Both families work the same basic way. They start from pure…
Implicit Generator Matching Distills Diffusion to One Step Read More »
Super resolution, the task of turning a low resolution image into a high resolution one, is familiar territory for ordinary photos. Hyperspectral imaging changes the game in two ways that the paper from Zhixiong Yang and colleagues at the National…
BKSR: A Unified Loop for Blind Hyperspectral Image Super Resolution Read More »
Semantic segmentation, the task of deciding which class every pixel belongs to, has always carried an awkward cost. To train a supervised model you need someone to color in every pixel of every training image across a fixed list of…
How iSeg Refines Stable Diffusion Attention for Segmentation Read More »
Reconstructing a static object from a scan is already a well studied problem, going back to foundational techniques like Marching Cubes and Poisson Surface Reconstruction. Add motion to the picture and the difficulty compounds. Early systems such as DynamicFusion, VolumeDeform,…
Motion2VecSets: Teaching AI to Guess the Missing Motion in 3D Scans Read More »
Image fusion sounds like a simple ask. Take an object out of one photo and put it convincingly into another. Anyone who has actually tried it in Photoshop knows how quickly convincing becomes the hard part. A pasted object needs…
DreamFuse Teaches Diffusion Models Real Image Fusion Read More »
Text to motion systems have gotten genuinely good at turning a sentence like walk forward and turn right into a plausible looking animated character. The trouble shows up at the edges of what language is good for. A prompt like…
DrawMotion Turns a Doodle Into 3D Character Motion Read More »
Interest in RNA structure has been climbing for a reason that has nothing to do with academic curiosity. RNA based therapeutics are now a real category of medicine, and designing an RNA molecule with a specific function starts with knowing…
How Base Pair Conditioning Lets RNAbpFlow Skip the MSA Read More »
All in one image restoration is the idea of training a single network that can fix rain, haze, low light, snow, blur, or noise without needing a separate specialist model for each. It works well when a test image genuinely…
How Stochastic Transport Fixes Composite Image Restoration Read More »
RigFace edits expression, pose, and lighting in portraits while preserving identity, by injecting identity features through self-attention instead of cross-attention in a diffusion model.
Why RigFace Injects Identity Through Self-Attention Instead of Cross-Attention Read More »