  We introduce SABR-CLIMB, a novel video model simulating human movement in
rock climbing environments using a virtual avatar. Our diffusion transformer
predicts the sample instead of noise in each diffusion step and ingests entire
videos to output complete motion sequences. By leveraging a large proprietary
dataset, NAV-22M, and substantial computational resources, we showcase a proof
of concept for a system to train general-purpose virtual avatars for complex
tasks in robotics, sports, and healthcare.
