OMG-Bench: A New Challenging Benchmark for Skeleton-based Online Micro Hand Gesture Recognition

TL;DR

This paper introduces OMG-Bench, a large-scale benchmark dataset for skeleton-based online micro hand gesture recognition, and proposes HMATr, a hierarchical memory transformer framework that improves detection accuracy.

Contribution

The paper presents the first large-scale micro gesture dataset and a novel hierarchical memory transformer model for improved online gesture recognition.

Findings

HMATr outperforms existing methods by 7.6% in detection rate.

OMG-Bench contains 13,948 instances of 40 fine-grained gestures.

The proposed pipeline enables automatic skeleton data generation for micro gestures.

Abstract

Online micro gesture recognition from hand skeletons is critical for VR/AR interaction but faces challenges due to limited public datasets and task-specific algorithms. Micro gestures involve subtle motion patterns, which make constructing datasets with precise skeletons and frame-level annotations difficult. To this end, we develop a multi-view self-supervised pipeline to automatically generate skeleton data, complemented by heuristic rules and expert refinement for semi-automatic annotation. Based on this pipeline, we introduce OMG-Bench, the first large-scale public benchmark for skeleton-based online micro gesture recognition. It features 40 fine-grained gesture classes with 13,948 instances across 1,272 sequences, characterized by subtle motions, rapid dynamics, and continuous execution. To tackle these challenges, we propose Hierarchical Memory-Augmented Transformer (HMATr), an end-to-end framework that unifies gesture detection and classification by leveraging hierarchical memory banks which store frame-level details and window-level semantics to preserve historical context. In addition, it employs learnable position-aware queries initialized from the memory to implicitly encode gesture positions and semantics. Experiments show that HMATr outperforms state-of-the-art methods by 7.6% in detection rate, establishing a strong baseline for online micro gesture recognition. Project page: https://omg-bench.github.io/