SyncUp：基于视觉的同步舞蹈练习支持
SyncUp: Vision-based Practice Support for Synchronized Dancing
摘要：
同步舞蹈的美在于多个舞者身体动作的同步。虽然舞者利用摄像机记录他们的练习，但标准视频界面不能有效地支持他们识别他们没有很好同步的片段的活动。这因此未能闭合迭代实践过程的紧密循环（即，捕捉练习、回顾视频、并再次练习）。我们提出了SyncUp，一个系统，提供多种交互式可视化，以支持同步舞蹈的做法，并解放用户从手动检查记录的实践视频。通过分析用户上传的视频，SyncUp量化了舞蹈同步的两个方面：多个舞者之间的姿势相似性和他们动作的时间对齐。然后，系统会突出显示哪些身体部位和舞蹈动作的哪些部分需要进一步练习，以实现更好的同步。我们的系统评估的结果表明，我们的姿势相似性估计和时间对齐预测与人类评级相关。我们的定性用户评估的参与者表达了SyncUp的好处及其潜在用途，确认它可以实现快速迭代实践。
英文摘要：
The beauty of synchronized dancing lies in the synchronization of body movements among multiple dancers. While dancers utilize camera recordings for their practice, standard video interfaces do not efficiently support their activities of identifying segments where they are not well synchronized. This thus fails to close a tight loop of an iterative practice process (i.e., capturing a practice, reviewing the video, and practicing again). We present SyncUp, a system that provides multiple interactive visualizations to support the practice of synchronized dancing and liberate users from manual inspection of recorded practice videos. By analyzing videos uploaded by users, SyncUp quantifies two aspects of synchronization in dancing: pose similarity among multiple dancers and temporal alignment of their movements. The system then highlights which body parts and which portions of the dance routine require further practice to achieve better synchronization. The results of our system evaluations show that our pose similarity estimation and temporal alignment predictions were correlated well with human ratings. Participants in our qualitative user evaluation expressed the benefits and its potential use of SyncUp, confirming that it would enable quick iterative practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
