StableHand: Quality-Aware Flow Matching for World-Space Dual-Hand Motion Estimation from Egocentric Video

Huajian Zeng1, Chaohua Yao2, Yuantai Zhang1, Jiaqi Yang1, Rolandos Alexandros Potamias3, Xingxing Zuo1,†
1Mohamed bin Zayed University of Artificial Intelligence (MBZUAI),   2University of Illinois at Urbana-Champaign (UIUC),   3Imperial College London
†Corresponding author
Paper arXiv Video Code
StableHand teaser

StableHand recovers world-space dual-hand motion from egocentric video, anchoring reliable hand observations and regenerating unreliable ones from a learned bimanual motion prior.

Abstract

Recovering world-space 4D motion of two interacting hands from egocentric video is a fundamental capability for supervising robot policy learning, where wrist trajectories track the end-effector and finger articulations specify the grasp pose. Two major challenges arise in this setting: hands frequently leave the camera view for extended periods due to head motion, and persistent hand–object interactions cause severe occlusions of one or both hands. Existing methods uniformly condition on noisy hand motion observations without accounting for their per-frame reliability, leading to substantial performance degradation.

Our key insight is that accurate world-space hand motion estimation is tightly coupled with the quality of per-frame hand observations. To this end, we decompose the quality of hand observations extracted from an off-the-shelf hand pose estimator into four channels: wrist global translation and finger articulations for both hands. We propose StableHand, a quality-aware flow-matching framework conditioned on these four-channel quality signals, which are predicted by a learned quality network. We naturally incorporate the quality signals into the flow-matching process through a per-channel forward schedule, a quality-adjusted velocity target, AdaLN modulation of the DiT denoiser, and a quality-aware ODE initialization. This unified generative process preserves high-quality observations while reconstructing unreliable ones using a learned bimanual motion prior.

Experiments on HOT3D and ARCTIC, two egocentric benchmarks featuring long missing-hand spans and persistent hand–object occlusions, show that StableHand achieves state-of-the-art performance across all reported metrics, reducing W-MPJPE by 20–25% compared to the strongest baseline, with the largest gains on heavily occluded ARCTIC sequences.

Pipeline Overview

StableHand pipeline

From an egocentric video, frozen off-the-shelf modules produce per-hand MANO observations together with camera and scene context. StableHand consists of two learned modules:

  • Quality Network — Predicts a per-component error (wrist and fingers of each hand), then converts it to a four-channel quality signal via an adaptively calibrated RBF bandwidth.
  • Quality-Aware Flow-Matching DiT — The quality signal drives a per-channel noise schedule, a quality-adjusted velocity target, AdaLN modulation of a four-token-per-frame DiT, and a quality-aware ODE initialization. High-quality channels are anchored on the observation while low-quality ones are regenerated from the learned bimanual motion prior.

Qualitative Results

HOT3D: Long Missing-Hand Spans

World-space dual-hand trajectories on HOT3D.

StableHand re-anchors when observations resume, while the camera-view WiLoR baseline drifts across long missing-hand spans.

ARCTIC: Persistent Hand–Object Occlusion

World-space dual-hand trajectories on ARCTIC.

Under persistent bimanual occlusion, StableHand preserves both hands' trajectories while baselines collapse the occluded hand to a generic prior.

More Qualitative Results

BibTeX

@article{zeng2026stablehand,
      title = {StableHand: Quality-Aware Flow Matching for World-Space Dual-Hand Motion Estimation from Egocentric Video},
      author = {Zeng, Huajian and Yao, Chaohua and Zhang, Yuantai and Yang, Jiaqi and Potamias, Rolandos Alexandros and Zuo, Xingxing},
      year = {2026},
    }