Stable Hand Pose Estimation under Tremor via Graph Neural Network

Abstract

Hand pose estimation, which predicts the spatial location of hand joints, is a fundamental task in VR/AR applications. Although existing methods can recover hand pose competently, the tremor issue occurring in hand motion has not been completely solved. Tremor is an involuntary motion accompanied by a desired gesture or hand motion, leading to hand pose that deviates from user's intentions. Considering the characteristic of tremor motion, we present a novel Graph Neural Network for stable 3D hand pose estimation. The input is depth images. The constraint adjacency matrix is devised in Graph Neural Network for dynamically adjusting the topology of a hand graph during message passing and aggregation. Firstly, since there are rich potential constraints among hand joints, we utilize the constraint adjacency matrix to mine the suitable topology, modeling spatial-temporal constraints of joints and outputting the precise tremor hand pose as the pre-estimation result. Then, for obtaining a stable hand pose, we provide a tremor compensation module based on the constraint adjacency matrix, which exploits the constraint between control points and tremor hand pose. Concretely, the control points represented the voluntary motion are employed as constraints to edit the tremor hand pose. Our extensive quantitative and qualitative experiments show that the proposed method has achieved decent performance for 3D tremor hand pose estimation.