A model trained on real-world egocentric video and body pose predicts video from human actions using an auto-regressive conditional diffusion transformer, evaluated with a hierarchical protocol of tasks.
We train models to Predict Ego-centric Video from human Actions (PEVA), given
the past video and an action represented by the relative 3D body pose. By
conditioning on kinematic pose trajectories, structured by the joint hierarchy
of the body, our model learns to simulate how physical human actions shape the
environment from a first-person point of view. We train an auto-regressive
conditional diffusion transformer on Nymeria, a large-scale dataset of
real-world egocentric video and body pose capture. We further design a
hierarchical evaluation protocol with increasingly challenging tasks, enabling
a comprehensive analysis of the model’s embodied prediction and control
abilities. Our work represents an initial attempt to tackle the challenges of
modeling complex real-world environments and embodied agent behaviors with
video prediction from the perspective of a human.
