DreamID-V is a new AI method that swaps faces in videos while keeping the body movements, expressions, lighting, and background steady and natural.
A digital twin is a living computer copy of a real thing (like a bridge, a heart, or a factory) that stays in sync with sensors and helps us predict, fix, and improve the real thing.
This paper shows how to give AI a steady “mental map” of the world that keeps updating even when the camera looks away.
This paper shows how to get strong text embeddings from decoder-only language models without any training.
Modern AI models can get very good at being correct, but in the process they often lose their ability to think in many different ways.
The paper introduces Fast-weight Product Key Memory (FwPKM), a memory layer that can quickly learn from the current text it reads, not just from past training.
This paper builds a real-time talking-listening head avatar that reacts naturally to your words, tone, nods, and smiles in about half a second.
CPPO is a new way to fine‑tune vision‑language models so they see pictures more accurately before they start to reason.
This paper shows that when teaching image generators with reinforcement learning, only a few early, very noisy steps actually help the model learn what people like.
Deep Delta Learning (DDL) replaces the usual “add the shortcut” rule in deep networks with a smarter, learnable move that can gently erase old info and write new info along a chosen direction.
MorphAny3D is a training-free way to smoothly change one 3D object into another, even if they are totally different (like a bee into a biplane).
SpaceTimePilot is a video AI that lets you steer both where the camera goes (space) and how the action plays (time) from one input video.