DeepDance: Motion capture data of improvised dance (2019)
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When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. (2022). DeepDance: Motion capture data of improvised dance (2019) (version 1.0). Zenodo 10.5281/zenodo.5838179 Abstract This dataset comprises full-body motion capture of dance as well as corresponding audio files. Nine dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 250Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 162 motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files. Music Skarphedinsson, M. Wallace, B. (2019). “Song a” Skarphedinsson, M. Wallace, B. (2019). “Song b” Skarphedinsson, M. Wallace, B. (2019). “Song c” Skarphedinsson, M. Wallace, B. (2019). “Song d” Skarphedinsson, M. Wallace, B. (2019). “Song f” LaClair, J. Bounce. Jesse LaClair, (2018) Referenced here as “Song e” Data Description The following data types are provided: Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files. Stimuli: audio .wav files containing 1 minute of the tracks described above. Code: MATLAB script for visualizing the motion capture files. Post-processing: The motion capture files are downsampled to 30 fps. Body segments have been standardized across all dancers in the data set and normalized so that the root marker is set at the origin. For further details please see [2]. Acknowledgements This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
使用本资源时，请引用：Wallace, B.、Nymoen, K.、Martin, C.P. 与 Tøressen, J.（2022）。《DeepDance：即兴舞蹈动作捕捉数据（2019）》（版本1.0）。Zenodo，DOI:10.5281/zenodo.5838179。 摘要 本数据集包含完整的人体舞蹈动作捕捉（motion capture）数据及对应音频文件。共招募9名舞者，分别针对6段不同音频进行即兴舞蹈录制。实验采用Qualisys红外光学动作捕捉系统（Qualisys），以毫米（mm）为单位、250Hz的采样率采集动作数据。该实验于2019年10月在奥斯陆大学完成。每名舞者针对每段乐曲完成3次表演录制，最终共得到162份动作捕捉文件。本数据集旨在作为动作生成深度学习任务的训练数据，同时附带用于可视化动作捕捉文件的MATLAB代码。 音频曲目：Skarphedinsson, M. 与 Wallace, B.（2019）创作的《Song a》、《Song b》、《Song c》、《Song d》、《Song f》；LaClair, J.的《Bounce》（Jesse LaClair，2018），本数据集中将其称为《Song e》。 数据说明 本数据集提供以下类型的数据： 1. 动作（标记点位置）：通过Qualisys Track Manager采集，以制表符分隔的.tsv格式文件存储。 2. 刺激音频：时长为1分钟的上述对应曲目.wav格式音频文件。 3. 代码：用于可视化动作捕捉文件的MATLAB脚本。 4. 后处理结果：动作捕捉文件已下采样至30fps；数据集中所有舞者的身体段已完成标准化处理，并将根标记点归一化至坐标原点。如需了解更多细节，请参阅文献[2]。 致谢 本研究部分受挪威研究理事会卓越中心计划资助（项目编号：262762）。 利益冲突 作者声明本研究未存在任何可能被视为潜在利益冲突的商业或财务关联。
