SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
<p>Data used for the paper <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> </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}, 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