AI
AI-generated videos often look great frame by frame—but the motion between frames can jitter, drift, or break physics. A new study introduces Motive, a motion-first way to find which training clips actually drive how a model moves. Instead of focusing on static appearance, Motive uses gradient-based influence signals weighted
AI
What if you could guide a robot by drawing arrows that show pushes and pulls? Goal Force is a new way to steer video world models with physics, not vague text prompts. Instead of describing goals in words or target images, users sketch force vectors and intermediate dynamics. The model
AI
What’s new VideoAR is a new way to generate videos with AI that predicts the next frame, over and over, at multiple scales. By separating what happens within a frame (spatial) from what happens across frames (temporal), it aims to make video generation faster and more stable. * 3D multi-scale
AI
What if you could direct a video generator by doodling right on the frames? The paper introduces In-Video Instruction: instead of long, vague text prompts, you add visual cues—overlaid words, arrows, or motion paths—inside the image. Each cue acts as a concrete instruction tied to a specific object.
Robotics
What's new PhysWorld lets robots learn skills from AI-generated videos—without collecting any real robot data. How it works * Given a task and one photo of the scene, a video model "imagines" a demonstration. * PhysWorld reconstructs a physics-based world from that video. * An object-centric learner turns
Robotics
What if robots could learn new tasks just by watching AI-generated videos - and still obey physics? PhysWorld is a new framework that turns text-and-image prompts into physically executable robot skills. * It first generates a task-conditioned video from a single scene image and a command. * Then it reconstructs the scene&