EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR

TL;DR

EgoPoseFormer v2 introduces a transformer-based egocentric human motion estimation method with an auto-labeling system, achieving high accuracy and temporal consistency for AR/VR applications using large unlabeled datasets.

Contribution

The paper presents a novel transformer-based model with auto-labeling for scalable, accurate egocentric human motion estimation in AR/VR.

Findings

Outperforms state-of-the-art by 12.2% and 19.4% in accuracy

Reduces temporal jitter by over 50%

Auto-labeling improves wrist MPJPE by 13.1%

Abstract

Egocentric human motion estimation is essential for AR/VR experiences, yet remains challenging due to limited body coverage from the egocentric viewpoint, frequent occlusions, and scarce labeled data. We present EgoPoseFormer v2, a method that addresses these challenges through two key contributions: (1) a transformer-based model for temporally consistent and spatially grounded body pose estimation, and (2) an auto-labeling system that enables the use of large unlabeled datasets for training. Our model is fully differentiable, introduces identity-conditioned queries, multi-view spatial refinement, causal temporal attention, and supports both keypoints and parametric body representations under a constant compute budget. The auto-labeling system scales learning to tens of millions of unlabeled frames via uncertainty-aware semi-supervised training. The system follows a teacher-student schema to generate pseudo-labels and guide training with uncertainty distillation, enabling the model to generalize to different environments. On the EgoBody3M benchmark, with a 0.8 ms latency on GPU, our model outperforms two state-of-the-art methods by 12.2% and 19.4% in accuracy, and reduces temporal jitter by 22.2% and 51.7%. Furthermore, our auto-labeling system further improves the wrist MPJPE by 13.1%.