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<p>Hand gestures form an integral part of human communication and their complexity makes their study and generation difficult. Here, we present a dataset comprising 2373 annotated gestures, designed to facilitate in-depth analysis of human communication. We captured these gestures from nine speakers across three distinct categories: University lecturers, Politicians, and Psychotherapists. The annotations encompass various aspects, including gesture types (e.g., metaphoric, iconic), descriptive terms characterizing each gesture (e.g., ‘sweep’, ‘container’), and their corresponding verbal utterances. The dataset also includes detailed physical properties such as hand height, distance to the body, arm angle, hand configuration, palm orientation, repetitions, size, and speed, alongside 3D pose tracking data. Where possible, video recordings provide additional multimodal context. Notably, we identified several previously undocumented lexemes, expanding the current lexicon of gesture research. This dataset offers a valuable resource for studying human communication, training models for gesture recognition and generation, and designing socially intelligent virtual agents.</p>

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A richly annotated dataset of co-speech hand gestures across diverse speaker contexts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月5日

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  • Laura B. Hensel,
  • Stephanie Cheng,
  • Stacy Marsella

摘要

Hand gestures form an integral part of human communication and their complexity makes their study and generation difficult. Here, we present a dataset comprising 2373 annotated gestures, designed to facilitate in-depth analysis of human communication. We captured these gestures from nine speakers across three distinct categories: University lecturers, Politicians, and Psychotherapists. The annotations encompass various aspects, including gesture types (e.g., metaphoric, iconic), descriptive terms characterizing each gesture (e.g., ‘sweep’, ‘container’), and their corresponding verbal utterances. The dataset also includes detailed physical properties such as hand height, distance to the body, arm angle, hand configuration, palm orientation, repetitions, size, and speed, alongside 3D pose tracking data. Where possible, video recordings provide additional multimodal context. Notably, we identified several previously undocumented lexemes, expanding the current lexicon of gesture research. This dataset offers a valuable resource for studying human communication, training models for gesture recognition and generation, and designing socially intelligent virtual agents.