Baker Ben, Liu Tony, Matelsky Jordan, Parodi Felipe, Mensh Brett, Krakauer John W, Kording Konrad
Davis AI Institute, Department of Philosophy, Colby College, Waterville, ME, United States.
Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, PA, United States.
Front Robot AI. 2024 May 2;11:1295308. doi: 10.3389/frobt.2024.1295308. eCollection 2024.
Dance plays a vital role in human societies across time and culture, with different communities having invented different systems for artistic expression through movement (genres). Differences between genres can be described by experts in words and movements, but these descriptions can only be appreciated by people with certain background abilities. Existing dance notation schemes could be applied to describe genre-differences, however they fall substantially short of being able to capture the important details of movement across a wide spectrum of genres. Our knowledge and practice around dance would benefit from a general, quantitative and human-understandable method of characterizing meaningful differences between aspects of any dance style; a computational kinematics of dance. Here we introduce and apply a novel system for encoding bodily movement as 17 macroscopic, interpretable features, such as expandedness of the body or the frequency of sharp movements. We use this encoding to analyze Hip Hop Dance genres, in part by building a low-cost machine-learning classifier that distinguishes genre with high accuracy. Our study relies on an open dataset (AIST++) of pose-sequences from dancers instructed to perform one of ten Hip Hop genres, such as Breakdance, Popping, or Krump. For comparison we evaluate moderately experienced human observers at discerning these sequence's genres from movements alone (38% where chance = 10%). The performance of a baseline, Ridge classifier model was fair (48%) and that of the model resulting from our automated machine learning pipeline was strong (76%). This indicates that the selected features represent important dimensions of movement for the expression of the attitudes, stories, and aesthetic values manifested in these dance forms. Our study offers a new window into significant relations of similarity and difference between the genres studied. Given the rich, complex, and culturally shaped nature of these genres, the interpretability of our features, and the lightweight techniques used, our approach has significant potential for generalization to other movement domains and movement-related applications.
舞蹈在不同时代和文化的人类社会中都发挥着至关重要的作用，不同社群创造了通过动作进行艺术表达的不同体系（流派）。流派之间的差异可以由专家用语言和动作来描述，但只有具备特定背景能力的人才能理解这些描述。现有的舞蹈记谱法可用于描述流派差异，然而，它们远远无法捕捉广泛流派中动作的重要细节。我们关于舞蹈的知识和实践将受益于一种通用、定量且人类可理解的方法，用于刻画任何舞蹈风格各方面之间有意义的差异；一种舞蹈的计算运动学。在此，我们引入并应用一种新颖的系统，将身体动作编码为17个宏观的、可解释的特征，例如身体的伸展程度或快速动作的频率。我们使用这种编码来分析嘻哈舞蹈流派，部分是通过构建一个低成本的机器学习分类器，该分类器能高精度地区分流派。我们的研究依赖于一个开放数据集（AIST++），该数据集包含舞者按照指令表演十种嘻哈流派（如霹雳舞、震感舞或小丑舞）之一的姿势序列。为作比较，我们评估了经验适度的人类观察者仅从动作辨别这些序列流派的能力（随机猜测正确率为10%时，他们的正确率为38%）。基线岭分类器模型的表现一般（48%），而我们自动化机器学习流程得出的模型表现出色（76%）。这表明所选特征代表了这些舞蹈形式中用于表达态度、故事和审美价值的动作的重要维度。我们的研究为所研究流派之间显著的异同关系提供了一个新窗口。鉴于这些流派丰富、复杂且受文化塑造的性质、我们特征的可解释性以及所使用的轻量级技术，我们的方法具有推广到其他运动领域和与运动相关应用的巨大潜力。
