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We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly.
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M. Burenius, J. Sullivan, and S. Carlsson · 2013
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Smpl: A skinned multi-person linear model
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https://www.noitom.com/ , 2015
Noitom Motion Capture Systems · 2015
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S. Andrews, I. Huerta, T. Komura, L. Sigal, and K. Mitchell · 2016
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F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero, and M. J. Black · 2016
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Model-based outdoor performance capture
N. Robertini, D. Casas, H. Rhodin, H.-P. Seidel, and C. Theobalt · 2016
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Human pose estimation from video and imus
T. Von Marcard, G. Pons-Moll, and B. Rosenhahn · 2016
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Towards accurate marker-less human shape and pose estimation over time
Y. Huang, F. Bogo, C. Lassner, A. Kanazawa, P. V. Gehler, J. Romero, I. Akhter, and M. J. Black · 2017
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Unite the people: Closing the loop between 3d and 2d human representations
C. Lassner, J. Romero, M. Kiefel, F. Bogo, M. J. Black, and P. V. Gehler · 2017
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
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Real-time full-body motion capture from video and imus
C. Malleson, A. Gilbert, M. Trumble, J. Collomosse, A. Hilton, and M. Volino · 2017
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Monocular 3d human pose estimation in the wild using improved cnn supervision
On the continuity of rotation representations in neural networks
Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li · 2019
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M. Habermann, W. Xu, M. Zollhofer, G. Pons-Moll, and C. Theobalt · 2020
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Resolving position ambiguity of imu-based human pose with a single rgb camera
T. Kaichi, T. Maruyama, M. Tada, and H. Saito · 2020
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Vibe: Video inference for human body pose and shape estimation
M. Kocabas, N. Athanasiou, and M. J. Black · 2020
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Real-time multi-person motion capture from multi-view video and imus
C. Malleson, J. Collomosse, and A. Hilton · 2020
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Fusion of multiple lidars and inertial sensors for the real-time pose tracking of human motion
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D. Mehta, H. Rhodin, D. Casas, P. Fua, O. Sotnychenko, W. Xu, and C. Theobalt · 2017
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Harvesting multiple views for marker-less 3d human pose annotations
G. Pavlakos, X. Zhou, K. G. Derpanis, and K. Daniilidis · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Hand keypoint detection in single images using multiview bootstrapping
T. Simon, H. Joo, I. Matthews, and Y. Sheikh · 2017
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Total capture: 3d human pose estimation fusing video and inertial sensors
M. Trumble, A. Gilbert, C. Malleson, A. Hilton, and J. Collomosse · 2017
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Sparse inertial poser: Automatic 3d human pose estimation from sparse imus
T. Von Marcard, B. Rosenhahn, M. J. Black, and G. Pons-Moll · 2017
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Twinfusion: High framerate non-rigid fusion through fast correspondence tracking
K. Guo, J. Taylor, S. Fanello, A. Tagliasacchi, M. Dou, P. Davidson, A. Kowdle, and S. Izadi · 2018
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A. K. Patil, A. Balasubramanyam, J. Y. Ryu, P. K. BN, B. Chakravarthi, and Y. H. Chai · 2020
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Eventcap: Monocular 3d capture of high-speed human motions using an event camera
L. Xu, W. Xu, V. Golyanik, M. Habermann, L. Fang, and C. Theobalt · 2020
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Residual force control for agile human behavior imitation and extended motion synthesis
Y. Yuan and K. Kitani · 2020
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Fusing wearable imus with multi-view images for human pose estimation: A geometric approach
Z. Zhang, C. Wang, W. Qin, and W. Zeng · 2020
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Ssn: Shape signature networks for multi-class object detection from point clouds
X. Zhu, Y. Ma, T. Wang, Y. Xu, J. Shi, and D. Lin · 2020
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Challencap: Monocular 3d capture of challenging human performances using multi-modal references
Y. He, A. Pang, X. Chen, H. Liang, M. Wu, Y. Ma, and L. Xu · 2021
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Pare: Part attention regressor for 3d human body estimation
M. Kocabas, C.-H. P. Huang, O. Hilliges, and M. J. Black · 2021
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Probabilistic modeling for human mesh recovery
N. Kolotouros, G. Pavlakos, D. Jayaraman, and K. Daniilidis · 2021
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Ai choreographer: Music conditioned 3d dance generation with aist++
R. Li, S. Yang, D. A. Ross, and A. Kanazawa · 2021
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Dynamics-regulated kinematic policy for egocentric pose estimation
Z. Luo, R. Hachiuma, Y. Yuan, and K. Kitani · 2021
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https://ouster.com/ , 2021
OUSTER High Performance Digital Lidar Solutions · 2021
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Humor: 3d human motion model for robust pose estimation
D. Rempe, T. Birdal, A. Hertzmann, J. Yang, S. Sridhar, and L. J. Guibas · 2021
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Transpose: real-time 3d human translation and pose estimation with six inertial sensors
X. Yi, Y. Zhou, and F. Xu · 2021
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Neural descent for visual 3d human pose and shape
A. Zanfir, E. G. Bazavan, M. Zanfir, W. T. Freeman, R. Sukthankar, and C. Sminchisescu · 2021
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Cylindrical and asymmetrical 3d convolution networks for lidar-based perception
X. Zhu, H. Zhou, T. Wang, F. Hong, W. Li, Y. Ma, H. Li, R. Yang, and D. Lin · 2021
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Stcrowd: A multimodal dataset for pedestrian perception in crowded scenes
P. Cong, X. Zhu, F. Qiao, Y. Ren, X. Peng, Y. Hou, L. Xu, R. Yang, D. Manocha, and Y. Ma · 2022
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Hsc4d: Human-centered 4d scene capture in large-scale indoor-outdoor space using wearable imus and lidar
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Lidarcap: Long-range marker-less 3d human motion capture with lidar point clouds
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Hybridcap: Inertia-aid monocular capture of challenging human motions
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Questsim: Human motion tracking from sparse sensors with simulated avatars
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Physical inertial poser (pip): Physics-aware real-time human motion tracking from sparse inertial sensors
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