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SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

<p>Data used for the paper&nbsp;<strong>SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data</strong></p> <p>It contains over <strong>1GB</strong> of high-quality<strong> motion capture data</strong> recorded with an Xsens Awinda system while using a variety of <strong>VR applications</strong> in Meta Quest devices.</p> <p>Visit the paper <a href="https://upc-virvig.github.io/SparsePoser/">website</a>!</p> <p>&nbsp;</p> <p><strong>If you find our data useful, please cite our paper:</strong></p> <p>@article{10.1145/3625264, author = {Ponton, Jose Luis and Yun, Haoran and Aristidou, Andreas and Andujar, Carlos and Pelechano, Nuria}, title = {SparsePoser: Real-Time Full-Body Motion Reconstruction from Sparse Data}, year = {2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {0730-0301}, url = {https://doi.org/10.1145/3625264}, doi = {10.1145/3625264},&nbsp;journal = {ACM Trans. Graph.}, month = {oct}}</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
0
Engagement
4

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