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Conventional methods for human pose estimation either require a high degree of instrumentation, by relying on many inertial measurement units (IMUs), or constraint the recording space, by relying on extrinsic cameras.
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C. Zheng, S. Zhu, M. Mendieta, T. Yang, C. Chen, and Z. Ding, “3d human pose estimation with spatial and temporal transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 656–11 665
2021
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X. Yi, Y. Zhou, and F. Xu, “Transpose: Real-time 3d human translation and pose estimation with six inertial sensors,” ACM Transactions on Graphics , vol. 40, no. 4, 08 2021
2021
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M. Kaufmann, Y. Zhao, C. Tang, L. Tao, C. Twigg, J. Song, R. Wang, and O. Hilliges, “Em-pose: 3d human pose estimation from sparse electromagnetic trackers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 11 510–11 520
2021
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Z. Zou and W. Tang, “Modulated graph convolutional network for 3d human pose estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 11 477–11 487
2021
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2021
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X. Yi, Y. Zhou, M. Habermann, S. Shimada, V. Golyanik, C. Theobalt, and F. Xu, “Physical inertial poser (pip): Physics-aware real-time human motion tracking from sparse inertial sensors,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022
2022
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