Generates physics-aware video world states for physical AI development using text prompts and multiple spatial control inputs derived from real-world data or simulation.
Generate exponentially large amounts of synthetic motion trajectories for robot manipulation from just a few human demonstrations.
Generalist model to generate future world state as videos from text and image prompts to create synthetic training data for robots and autonomous vehicles.
Generates future frames of a physics-aware world state based on simply an image or short video prompt for physical AI development.
Simulate, test, and optimize physical AI and robotic fleets at scale in industrial digital twins before real-world deployment.
Expressive and engaging English voices for Q&A assistants, brand ambassadors, and service robots
ProteinMPNN is a deep learning model for predicting amino acid sequences for protein backbones.