草图与骨架:视频注释可以捕捉动作捕捉无法捕捉的东西

M. Gillies, Harry Brenton, M. Yee-King, Andreu Grimalt-Reynes, M. d'Inverno
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引用次数: 6

摘要

良好的姿势对成功的音乐表演至关重要,音乐老师花了相当多的精力来改善学生的姿势。本文提出了一项用户研究,以评估骨骼运动捕捉系统(基于微软Kinect™),以支持教师在演奏乐器时向学习者提供有关其姿势和运动的反馈。该研究发现了骨骼动作捕捉的一些问题,这些问题可能使其不适合这种类型的反馈:捕捉中的小故障降低了对系统的信任,特别是当运动数据从其他有助于判断其是否正确的上下文线索中删除时;自动反馈可能无法解释不同身体比例的学习者所需要的游戏风格的多样性,最重要的是,除了最初级的初学者之外,骨架表示遗漏了许多检测姿势问题所需的线索。该研究还包括一个参与式设计阶段,该阶段产生了一个彻底重新设计的原型,该原型用一个界面取代了骨骼动作捕捉,该界面允许教师和学习者在计算机视觉跟踪的支持下在视频上进行素描。
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Sketches vs skeletons: video annotation can capture what motion capture cannot
Good posture is vital to successful musical performance and music teachers spend a considerable amount of effort on improving their students' posture. This paper presents a user study to evaluate a skeletal motion capture system (based on the Microsoft Kinect™) for supporting teachers as they give feedback to learners about their posture and movement whilst playing an instrument. The study identified a number of problems with skeletal motion capture that are likely to make it unsuitable for this type of feedback: glitches in the capture reduce trust in the system, particularly as the motion data is removed from other contextual cues that could help judge whether it is correct or not; automated feedback can fail to account for the diversity of playing styles required by learners of different physical proportions, and most importantly, the skeleton representation leaves out many cues that are required to detect posture problems in all but the most elementary beginners. The study also included a participatory design stage which resulted in a radically redesigned prototype, which replaced skeletal motion capture with an interface that allows teachers and learners to sketch on video with the support of computer vision tracking.
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