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HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition

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arxiv 2412.01508 v1 pith:3NBZIB63 submitted 2024-12-02 cs.CV

HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition

Anton Nuzhdin , Alexander Nagaev , Alexander Sautin , Alexander Kapitanov , Karina Kvanchiani This is my paper
classification cs.CV
keywords gesturehandrecognitiondynamicgestureshagridhagridv2version
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper proposes the second version of the widespread Hand Gesture Recognition dataset HaGRID -- HaGRIDv2. We cover 15 new gestures with conversation and control functions, including two-handed ones. Building on the foundational concepts proposed by HaGRID's authors, we implemented the dynamic gesture recognition algorithm and further enhanced it by adding three new groups of manipulation gestures. The ``no gesture" class was diversified by adding samples of natural hand movements, which allowed us to minimize false positives by 6 times. Combining extra samples with HaGRID, the received version outperforms the original in pre-training models for gesture-related tasks. Besides, we achieved the best generalization ability among gesture and hand detection datasets. In addition, the second version enhances the quality of the gestures generated by the diffusion model. HaGRIDv2, pre-trained models, and a dynamic gesture recognition algorithm are publicly available.

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Cited by 2 Pith papers

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  1. Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices

    cs.LG 2026-05 conditional novelty 4.0

    Fusing 8x8 ToF and IR sensors with a 6343-parameter CNN achieves 92.3% accuracy and 0.93 macro F1 on 7 static gestures while running at millisecond latency and 50 mW on STM32 MCUs.

  2. Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions

    cs.CV 2025-07 unverdicted novelty 2.0

    A literature review that categorizes deep learning approaches for visual hand gesture recognition, summarizes state-of-the-art methods across tasks, reviews datasets and metrics, and identifies challenges and future d...