HumanWM: A World Action Model for Egocentric Human Motion Reconstruction

Reconstructing human motion from egocentric video requires inferring a largely unseen body from viewpoint changes and interactions with the environment. We present HumanWM, a World Action Model for Egocentric Motion that adapts a pretrained world model to jointly model egocentric video and structured human actions. The model introduces Coupled Kinematic Supervision that jointly constrains spatial structure and acceleration for egocentric motion reconstruction. At inference, full-sequence optimization balances fidelity to predicted motion with rotational and root-dynamics regularization. On 3,000 sequences from 57 Nymeria test recordings, HumanWM outperforms all evaluated baselines across pose, root-trajectory, motion-dynamics, and foot-skating metrics using RGB alone. Compared with RGB-only ReViV, the RGB-only configuration reduces globally aligned and Procrustes-aligned mean per-joint position errors by 38.2% and 44.9%, respectively. Task text further improves pose and root-trajectory accuracy, yielding 78.10 mm GA-MPJPE, 46.49 mm PA-MPJPE, and 0.216 m final root trajectory error.