Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time
Open accessAbstract
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330 000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes. 1
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent approaches addressing the lack of annotated datasets extract virtual movement data from modalities such as mocap and videos, which contain a large number of participants and a diverse set of actions and environments.
“Recent approaches addressing the lack of annotated datasets involve extraction of virtual movement data from modalities such as mocap [37, 72] and videos [44], which contain a large number of participants and diverse set of actions and environments.”
From Contrastive Predictive Coding for Human Activity Recognition (2021) · cites this paper as [37]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634 · see citing works
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911 · see citing works
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734 · see citing works
Self-citationHuman3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653 · see citing works
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.214 · Cited by 3,288 · see citing works
Wei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.511 · Cited by 2,966 · see citing works
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,050 · see citing works
End-to-End Recovery of Human Shape and Pose
Kanazawa, A., Black, M. J., Jacobs, D. W., & Malik, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00744 · Cited by 1,899 · see citing works
Self-citationKeep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_34 · Cited by 1,502 · see citing works
Self-citationJoint Training of a Convolutional Network and a Graphical Model for Human Pose EstimationPreprint
Tompson, J., Jain, A., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2984 · Cited by 1,317 · see citing works
Sigal, L., Bălan, A. O., & Black, M. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0273-6 · Cited by 1,202 · see citing works
Self-citationMehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H., Xu, W., Casas, D., & Theobalt, C. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073596 · Cited by 1,156 · see citing works
Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera
von Marcard, T., Henschel, R. D., Black, M. J., Rosenhahn, B., & Pons‐Moll, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_37 · Cited by 1,062 · see citing works
Self-citationOn Human Motion Prediction Using Recurrent Neural Networks
Martínez, J., Black, M. J., & Romero, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.497 · Cited by 1,019 · see citing works
Self-citationDynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time
Newcombe, R. A., Fox, D., & Seitz, S. M. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298631 · Cited by 951 · see citing works
Tracking people with twists and exponential maps
Bregler, C., & Malik, J. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1998.698581 · Cited by 719 · see citing works
High-quality streamable free-viewpoint video
Collet, A., Chuang, M., Sweeney, P., Gillett, D., Evseev, D., Calabrese, D., Hoppe, H., Kirk, A. G., & Sullivan, S. (2015) · ACM Transactions on Graphics · doi:10.1145/2766945 · Cited by 577 · see citing works
Phase-functioned neural networks for character control
Holden, D., Komura, T., & Saito, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073663 · Cited by 556 · see citing works
Performance capture from sparse multi-view video
de Aguiar, E., Stoll, C., Theobalt, C., Ahmed, N., Seidel, H., & Thrun, S. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360697 · Cited by 535 · see citing works
peng (2017) · doi:10.1145/3072959.3073602 · Cited by 502 · see citing works
Dou, M., Khamis, S., Degtyarev, Y., Davidson, P., Fanello, S., Kowdle, A., Escolano, S. O., Rhemann, C., Kim, D., Taylor, J., et al. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925969 · Cited by 500 · see citing works
Neural Body Fitting: Unifying Deep Learning and Model Based Human Pose and Shape Estimation
Omran, M., Lassner, C., Pons-Moll, G., Gehler, P., & Schiele, B. (2018) · 2018 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00062 · Cited by 498 · see citing works
Self-citationPons‐Moll, G., Pujades, S., Hu, S., & Black, M. J. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073711 · Cited by 495 · see citing works
Self-citationVideo Based Reconstruction of 3D People Models
Alldieck, T., Magnor, M. A., Xu, W., Theobalt, C., & Pons‐Moll, G. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00875 · Cited by 468 · see citing works
Self-citationPose-conditioned joint angle limits for 3D human pose reconstruction
Akhter, I., & Black, M. J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298751 · Cited by 451 · see citing works
Self-citationSparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
Zhou, X., Zhu, M., Leonardos, S., Derpanis, K. G., & Daniilidis, K. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.537 · Cited by 414 · see citing works
Real-time non-rigid reconstruction using an RGB-D camera
Zollhöfer, M., Nießner, M., Izadi, S., Rehmann, C., Zach, C., Fisher, M., Wu, C., Fitzgibbon, A. W., Loop, C., Theobalt, C., et al. (2014) · ACM Transactions on Graphics · doi:10.1145/2601097.2601165 · Cited by 405 · see citing works
Single-Shot Multi-person 3D Pose Estimation from Monocular RGB
Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons‐Moll, G., & Theobalt, C. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00024 · Cited by 395 · see citing works
Self-citationPerformance animation from low-dimensional control signals
Chai, J., & Hodgins, J. K. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073248 · Cited by 349 · see citing works
Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation - SCA '04
(2004) · Computer animation and simulation · doi:10.1145/1028523 · Cited by 345 · see citing works
Loper, M., Mahmood, N., & Black, M. J. (2014) · ACM Transactions on Graphics · doi:10.1145/2661229.2661273 · Cited by 344 · see citing works
Self-citationPons‐Moll, G., Romero, J., Mahmood, N., & Black, M. J. (2015) · ACM Transactions on Graphics · doi:10.1145/2766993 · Cited by 323 · see citing works
Self-citation3D Human pose estimation: A review of the literature and analysis of covariates
Sarafianos, N., Boteanu, B. A., Ionescu, B. E., & Kakadiaris, I. A. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.09.002 · Cited by 323 · see citing works
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
von Marcard, T., Rosenhahn, B., Black, M. J., & Pons‐Moll, G. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13131 · Cited by 309 · see citing works
Self-citationTotal Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors
Trumble, M., Gilbert, A., Malleson, C., Hilton, A., & Collomosse, J. P. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.14 · Cited by 301 · see citing works
Motion Capture of Hands in Action Using Discriminative Salient Points
Ballan, L., Taneja, A., Gall, J., Gool, L. V., & Pollefeys, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_46 · Cited by 285 · see citing works
The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation
Taylor, J., Shotton, J., Sharp, T., & Fitzgibbon, A. W. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247664 · Cited by 272 · see citing works
Practical motion capture in everyday surroundings
Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Groß, M. H., Matusik, W., & Popović, J. (2007) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1275808.1276421 · Cited by 269 · see citing works
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Tekin, B., Márquez-Neila, P., Salzmann, M., & Fua, P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.425 · Cited by 241 · see citing works
Tracking the articulated motion of two strongly interacting hands
Oikonomidis, I., Kyriazis, N., & Argyros, A. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247885 · Cited by 231 · see citing works
Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database
Torre, F. D. L., Hodgins, J. K., Bargteil, A. W., Martin, X., Macey, J., Collado, A., & Beltran, P. (2018) · KiltHub Repository · doi:10.1184/r1/6555020 · Cited by 228 · see citing works
Towards Accurate Marker-Less Human Shape and Pose Estimation over Time
Huang, Y., Bogo, F., Lassner, C., Kanazawa, A., Gehler, P., Romero, J., Akhter, I., & Black, M. J. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00055 · Cited by 217 · see citing works
Self-citationPractical motion capture in everyday surroundings
Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Groß, M. H., Matusik, W., & Popović, J. (2007) · ACM Transactions on Graphics · doi:10.1145/1276377.1276421 · Cited by 217 · see citing works
Accurate realtime full-body motion capture using a single depth camera
Wei, X. K., Zhang, P., & Chai, J. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366207 · Cited by 215 · see citing works
Fast articulated motion tracking using a sums of Gaussians body model
Stoll, C., Hasler, N., Gall, J., Seidel, H., & Theobalt, C. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126338 · Cited by 213 · see citing works
Motion reconstruction using sparse accelerometer data
Tautges, J., Zinke, A., Krüger, B., Baumann, J., Weber⋆, A., Helten, T., Müller, M., Seidel, H., & Eberhardt, B. (2011) · ACM Transactions on Graphics · doi:10.1145/1966394.1966397 · Cited by 197 · see citing works
Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks
Hannink, J., Kautz, T., Pasluosta, C. F., Gasmann, K.-G., Klucken, J., & Eskofier, B. M. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636456 · Cited by 196 · see citing works
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
Chen, X., & Yuille, A. (2015) · Cited by 187 · see citing works
2017 International Conference on 3D Vision (3DV)
(2017) · doi:10.1109/3dv43410.2017 · Cited by 170 · see citing works
Human Pose Estimation from Video and IMUs
von Marcard, T., Pons‐Moll, G., & Rosenhahn, B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2522398 · Cited by 163 · see citing works
Self-citationRhodin, H., Richardt, C., Casas, D., Insafutdinov, E., Shafiei, M., Seidel, H., Schiele, B., & Theobalt, C. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2980235 · Cited by 156 · see citing works
Real-Time Human Pose Tracking from Range Data
Ganapathi, V., Plagemann, C., Koller, D., & Thrun, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_53 · Cited by 150 · see citing works
Model-based multiple view reconstruction of people
Starck, & Hilton, A. (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238446 · Cited by 146 · see citing works
Action capture with accelerometers
Slyper, R., & Hodgins, J. K. (2008) · Symposium on Computer Animation · doi:10.5555/1632592.1632620 · Cited by 143 · see citing works
Performance capture from sparse multi-view video
de Aguiar, E., Stoll, C., Theobalt, C., Ahmed, N., Seidel, H., & Thrun, S. (2008) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1399504.1360697 · Cited by 136 · see citing works
Multisensor-fusion for 3D full-body human motion capture
Pons‐Moll, G., Baak, A., Helten, T., Müller, M., Seidel, H., & Rosenhahn, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540153 · Cited by 131 · see citing works
Self-citationReal-Time Body Tracking with One Depth Camera and Inertial Sensors
Helten, T., Müller, M., Seidel, H., & Theobalt, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.141 · Cited by 107 · see citing works
Real-Time Full-Body Motion Capture from Video and IMUs
Malleson, C., Gilbert, A., Trumble, M., Collomosse, J. P., Hilton, A., & Volino, M. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00058 · Cited by 103 · see citing works
Realtime human motion control with a small number of inertial sensors
Liu, H., Wei, X. K., Chai, J., Ha, I., & Rhee, T. J. (2011) · ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) · doi:10.1145/1944745.1944768 · Cited by 98 · see citing works
A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation
Rhodin, H., Robertini, N., Richardt, C., Seidel, H., & Theobalt, C. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.94 · Cited by 90 · see citing works
Outdoor human motion capture using inverse kinematics and von mises-fisher sampling
Pons‐Moll, G., Baak, A., Gall, J., Leal-Taixé, L., Müller, M., Seidel, H., & Rosenhahn, B. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126375 · Cited by 86 · see citing works
Self-citationMARCOnI—ConvNet-Based MARker-Less Motion Capture in Outdoor and Indoor Scenes
Elhayek, A., de Aguiar, E., Jain, A., Thompson, J., Pishchulin, L., Andriluka, M., Bregler, C., Schiele, B., & Theobalt, C. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2557779 · Cited by 67 · see citing works
Real-time Physics-based Motion Capture with Sparse Sensors
Andrews, S., Huerta, I., Komura, T., Sigal, L., & Mitchell, K. (2016) · Conference on Visual Media Production · doi:10.1145/2998559.2998564 · Cited by 60 · see citing works
Recurrent Network Models for Human DynamicsPreprint
Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.00271 · Cited by 59 · see citing works
Metric Regression Forests for Correspondence Estimation
Pons‐Moll, G., Taylor, J., Shotton, J., Hertzmann, A., & Fitzgibbon, A. W. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0818-9 · Cited by 53 · see citing works
Self-citationSparse Inertial PoserPreprint
von MarcardT., RosenhahnB., BlackM., J., & Pons-MollG. (2017) · arXiv (Cornell University) · Cited by 42 · see citing works
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
Mousas, C. (2017) · Sensors · doi:10.3390/s17112589 · Cited by 33 · see citing works
Discriminative Human Full-Body Pose Estimation from Wearable Inertial Sensor Data
Schwarz, L., Mateus, D., & Navab, N. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10470-1_14 · Cited by 32 · see citing works
Fusing 2D Uncertainty and 3D Cues for Monocular Body Pose Estimation.Preprint
Tekin, B., Márquez-Neila, P., Salzmann, M., & Fua, P. (2016) · arXiv (Cornell University) · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
End-to-End Human Pose and Mesh Reconstruction with Transformers
Lin, K., Wang, L., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00199 · Cited by 740 · see citing works
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01315 · Cited by 553 · see citing works
STAR: Sparse Trained Articulated Human Body Regressor
Osman, A. A. A., Bolkart, T., & Black, M. J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58539-6_36 · Cited by 252 · see citing works
Self-citationHan, S., Liu, B., Cabezas, R., Twigg, C. D., Zhang, P., Petkau, J., Yu, T.-H., Tai, C.-J., Akbay, M., Wang, Z., et al. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392452 · Cited by 205 · see citing works
Yi, X., Zhou, Y., & Xu, F. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459786 · Cited by 202 · see citing works
Structured Prediction Helps 3D Human Motion Modelling
Aksan, E., Kaufmann, M., & Hilliges, O. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00724 · Cited by 194 · see citing works
Self-citationInteractive hand pose estimation using a stretch-sensing soft glove
Glauser, O., Wu, S., Panozzo, D., Hilliges, O., & Sorkine‐Hornung, O. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322957 · Cited by 171 · see citing works
Self-citationKwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3411841 · Cited by 152 · see citing works
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
Habibie, I., Xu, W., Mehta, D., Pons‐Moll, G., & Theobalt, C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01116 · Cited by 149 · see citing works
Self-citationContrastive Predictive Coding for Human Activity Recognition
Haresamudram, H., Essa, I., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3463506 · Cited by 150 · see citing works
“Recent approaches addressing the lack of annotated datasets involve extraction of virtual movement data from modalities such as mocap [37, 72] and videos [44], which contain a large number of participants and diverse set of actions and environments.”
BackgroundDorschky, E., Nitschke, M., Martindale, C. F., van den Bogert, A. J., Koelewijn, A. D., & Eskofier, B. M. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00604 · Cited by 137 · see citing works
Guzov, V., Mir, A., Sattler, T., & Pons‐Moll, G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00430 · Cited by 137 · see citing works
Self-citationDeep Learning for Inertial Positioning: A Survey
Chen, C., & Pan, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3381161 · Cited by 145 · see citing works
A Survey of Human Gait-Based Artificial Intelligence Applications
Harris, E., Khoo, I., & Demircan, E. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.749274 · Cited by 136 · see citing works
Prediction of lower limb joint angles and moments during gait using artificial neural networks
Mundt, M., Thomsen, W., Witter, T., Koeppe, A., David, S., Bamer, F., Potthast, W., & Markert, B. A. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02061-3 · Cited by 133 · see citing works
AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing
Jiang, J., Streli, P., Qiu, H., Fender, A. R., Laich, L., Snape, P., & Holz, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20065-6_26 · Cited by 128 · see citing works
InterGen: Diffusion-Based Multi-human Motion Generation Under Complex Interactions
Liang, H., Zhang, W., Li, W., Jingyi Yu, A., & Xu, L. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02042-6 · Cited by 128 · see citing works
QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars
Winkler, A., Won, J., & Ye, Y. (2022) · SIGGRAPH Asia Conference Papers · doi:10.1145/3550469.3555411 · Cited by 123 · see citing works
MoVi: A large multi-purpose human motion and video dataset
Ghorbani, S., Mahdaviani, K., Thaler, A., Kording, K. P., Cook, D. J., Blohm, G., & Troje, N. F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253157 · Cited by 120 · see citing works
Ahuja, K., Jiang, Y., Goel, M., & Harrison, C. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445138 · Cited by 119 · see citing works
IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds
Mollyn, V., Arakawa, R., Goel, M., Harrison, C., & Ahuja, K. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581392 · Cited by 106 · see citing works
Jiang, Y., Ye, Y., Gopinath, D., Won, J., Winkler, A. W., & Liu, C. K. (2022) · SIGGRAPH Asia Conference Papers · doi:10.1145/3550469.3555428 · Cited by 102 · see citing works
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Du, Y., Kips, R., Pumarola, A., Starke, S., Thabet, A., & Sanakoyeu, A. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00054 · Cited by 103 · see citing works
X-Avatar: Expressive Human Avatars
Shen, K., Guo, C., Kaufmann, M., Zarate, J., Valentin, J., Song, J., & Hilliges, O. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01622 · Cited by 86 · see citing works
Self-citationSelfPose: 3D Egocentric Pose Estimation From a Headset Mounted Camera
Tomè, D., Alldieck, T., Peluse, P., Pons‐Moll, G., Agapito, L., Badino, H., & la Torre, F. D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3029700 · Cited by 82 · see citing works
Self-citationLoBSTr: Real‐time Lower‐body Pose Prediction from Sparse Upper‐body Tracking Signals
Yang, D., Kim, D., & Lee, S. (2021) · Computer Graphics Forum · doi:10.1111/cgf.142631 · Cited by 65 · see citing works
Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count
Sy, L. W., Raitor, M., Rosario, M. D., Khamis, H., Kark, L., Lovell, N. H., & Redmond, S. J. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3026464 · Cited by 64 · see citing works
EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild
Kaufmann, M., Song, J., Guo, C., Shen, K., Jiang, T., Tang, C., Zarate, J., & Hilliges, O. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01345 · Cited by 60 · see citing works
Self-citationA Joint Global–Local Network for Human Pose Estimation With Millimeter Wave Radar
Cao, Z., Ding, W., Chen, R., Zhang, J., Guo, X., & Wang, G. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3201005 · Cited by 59 · see citing works
Real-Time Multi-person Motion Capture from Multi-view Video and IMUs
Malleson, C., Collomosse, J. P., & Hilton, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01270-5 · Cited by 56 · see citing works
A survey on monocular 3D human pose estimation
Ji, X., Fang, Q., Dong, J., Shuai, Q., Jiang, W., & Zhou, X. (2020) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2020.04.005 · Cited by 57 · see citing works
LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors
Ren, Y., Zhao, C., He, Y., Cong, P., Liang, H., Jingyi Yu, A., Xu, L., & Ma, Y. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3247088 · Cited by 52 · see citing works
MIME: Human-Aware 3D Scene Generation
Yi, H., Huang, C.-H. P., Tripathi, S., Hering, L., Thies, J., & Black, M. J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01246 · Cited by 51 · see citing works
Self-citationBicer, M., Phillips, A., Melis, A., McGregor, A. H., & Modenese, L. (2022) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2022.111301 · Cited by 50 · see citing works
Pei, L., Xia, S., Chu, L., Xiao, F., Wu, Q., Yu, W., & Qiu, R. C. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3055859 · Cited by 50 · see citing works
TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial SensorsPreprint
Yi, X., Zhou, Y., & Xu, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04605 · Cited by 49 · see citing works
SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
Pontón, J. L., Yun, H., Aristidou, A., Andújar, C., & Pelechano, N. (2023) · ACM Transactions on Graphics · doi:10.1145/3625264 · Cited by 48 · see citing works
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
Xiao, F., Pei, L., Chu, L., Zou, D., Yu, W., Zhu, Y., & Li, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69873-7_19 · Cited by 49 · see citing works
SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments
Dai, Y., Lin, Y., Lin, X., Wen, C., Xu, L., Yi, H., Shen, S., Ma, Y., & Wang, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00073 · Cited by 45 · see citing works
MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting
Tessler, C., Guo, Y., Nabati, O., Chechik, G., & Peng, X. B. (2024) · ACM Transactions on Graphics · doi:10.1145/3687951 · Cited by 44 · see citing works
Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling
Zheng, X., Su, Z., Wen, C., Xue, Z., & Jin, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01349 · Cited by 41 · see citing works
Bidirectional recurrent autoencoder for 3D skeleton motion data refinement
Li, S., Zhou, Y., Zhu, H., Xie, W., Zhao, Y., & Liu, X. (2019) · Computers & Graphics · doi:10.1016/j.cag.2019.03.010 · Cited by 41 · see citing works
EM-POSE: 3D Human Pose Estimation from Sparse Electromagnetic Trackers
Kaufmann, M., Zhao, Y., Tang, C., Tao, L., Twigg, C. D., Song, J., Wang, R. Y., & Hilliges, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01131 · Cited by 41 · see citing works
Self-citationPontón, J. L., Yun, H., Andújar, C., & Pelechano, N. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14628 · Cited by 40 · see citing works
Xu, V., Gao, C., Hoffmann, H., & Ahuja, K. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676461 · Cited by 40 · see citing works
Xia, S., Chu, L., Pei, L., Zhang, Z., Yu, W., & Qiu, R. C. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3111996 · Cited by 40 · see citing works
Liu, Y., Zhang, S., & Gowda, M. (2021) · Proceedings of the International Conference on Internet-of-Things Design and Implementation · doi:10.1145/3450268.3453537 · Cited by 39 · see citing works
ControllerPose: Inside-Out Body Capture with VR Controller Cameras
Ahuja, K., Shen, V., Fang, C. M., Riopelle, N., Kong, A., & Harrison, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502105 · Cited by 39 · see citing works
SUPR: A Sparse Unified Part-Based Human Representation
Osman, A. A. A., Bolkart, T., Tzionas, D., & Black, M. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20086-1_33 · Cited by 39 · see citing works
Self-citationNymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild
Ma, L., Ye, Y., Hong, F., Guzov, V., Jiang, Y., Postyeni, R., Pesqueira, L., Gamino, A., Baiyya, V. B., Kim, H. J., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72691-0_25 · Cited by 43 · see citing works
Beyond Supervised Learning for Pervasive Healthcare
Gu, X., Deligianni, F., Han, J., Liu, X., Chen, W., Yang, G., & Lo, B. P. L. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3296938 · Cited by 41 · see citing works
SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data
DeVrio, N., Mollyn, V., & Harrison, C. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606821 · Cited by 35 · see citing works
Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles
Saini, N., Price, E., Tallamraju, R., Enficiaud, R., Ludwig, R., Martinovic, I., Ahmad, A., & Black, M. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00091 · Cited by 35 · see citing works
Self-citationResolving Position Ambiguity of IMU-Based Human Pose with a Single RGB Camera
Kaichi, T., Maruyama, T., Tada, M., & Saitô, H. (2020) · Sensors · doi:10.3390/s20195453 · Cited by 35 · see citing works
Wang, Y., Ren, Y., Chen, Y. J., & Yang, J. (2022) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3560905.3568536 · Cited by 35 · see citing works
Zhang, Y. L., Xia, S., Chu, L., Yang, J., Wu, Q., & Pei, L. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00185 · Cited by 34 · see citing works
Geissinger, J., & Asbeck, A. T. (2020) · Sensors · doi:10.3390/s20216330 · Cited by 32 · see citing works
Armani, R., Qian, C., Jiang, J., & Holz, C. (2024) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3641519.3657465 · Cited by 31 · see citing works
Wouwe, T. V., Lee, S., Falisse, A. M. D., Delp, S. L., & Liu, C. K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00243 · Cited by 32 · see citing works
Wearable Motion Capture: Reconstructing and Predicting 3D Human Poses From Wearable Sensors
Moniruzzaman, M., Yin, Z., Hossain, M. S. B., Choi, H. Y., & Guo, Z. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3311448 · Cited by 29 · see citing works
Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time
Kim, M., & Lee, S. (2022) · Sensors · doi:10.3390/s22134846 · Cited by 29 · see citing works
SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures
Sharma, A., Salchow-Hömmen, C., Mollyn, V., Nittala, A. S., Hedderich, M. A., Koelle, M., Seel, T., & Steimle, J. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3569894 · Cited by 29 · see citing works
HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations
Dai, P., Zhang, Y., Liu, T., Fan, Z., Du, T., Su, Z., Zheng, X., & Li, Z. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00089 · Cited by 28 · see citing works
Accurate and Fast Classification of Foot Gestures for Virtual Locomotion
Shi, X., Pan, J., Hu, Z., Lin, J., Guo, S., Liao, M., Pan, Y., & Liu, L. (2019) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2019.000-6 · Cited by 27 · see citing works
Wearable Inertial Sensor-Based Limb Lameness Detection and Pose Estimation for Horses
Yigit, T., Han, F., Rankins, E. M., Yi, J., McKeever, K. H., & Malinowski, K. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3157793 · Cited by 27 · see citing works
Zhou, B., Geißler, D., Faulhaber, M., Gleiß, C., Zahn, E. F., Ray, L. S. S., Gamarra, D. F., Rey, V. F., Suh, S., Bian, S., et al. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580883 · Cited by 27 · see citing works
CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions
Yan, M., Wang, X., Dai, Y., Shen, S., Wen, C., Xu, L., Ma, Y., & Wang, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01247 · Cited by 27 · see citing works
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
Castillo, A., Escobar, M., Jeanneret, G., Pumarola, A., Arbeláez, P., Thabet, A., & Sanakoyeu, A. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00456 · Cited by 30 · see citing works
Distribution-Aligned Diffusion for Human Mesh Recovery
Foo, L. G., Gong, J. L., Rahmani, H., & Liu, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00846 · Cited by 28 · see citing works
Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency
Yun, H., Pontón, J. L., Andújar, C., & Pelechano, N. (2023) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr55154.2023.00044 · Cited by 26 · see citing works
Fusion of Video and Inertial Sensing Data via Dynamic Optimization of a Biomechanical Model
Pearl, O., Shin, S., Godura, A., Bergbreiter, S., & Halilaj, E. (2022) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2023.111617 · Cited by 26 · see citing works
3D human pose estimation with single image and inertial measurement unit (IMU) sequence
Liu, L., Yang, J., Lin, Y., Zhang, P., & Zhang, L. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110175 · Cited by 26 · see citing works
Zhang, X., Zhang, H., Hu, J., Deng, J., & Wang, Y. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3300279 · Cited by 26 · see citing works
Fast Human Motion reconstruction from sparse inertial measurement units considering the human shape
Xiao, X., Wang, J., Feng, P., Gong, A., Zhang, X., & Zhang, J. (2024) · Nature Communications · doi:10.1038/s41467-024-46662-5 · Cited by 25 · see citing works
From Methods to Applications: A Review of Deep 3D Human Motion Capture
Niu, Z., Lü, K., Xue, J., Qin, X., Wang, J., & Shao, L. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3423411 · Cited by 25 · see citing works
Dai, Y., Lin, Y., Wen, C., Shen, S., Xu, L., Jingyi Yu, A., Ma, Y., & Wang, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00667 · Cited by 25 · see citing works
Sy, L. W., Lovell, N. H., & Redmond, S. J. (2020) · Sensors · doi:10.3390/s20236829 · Cited by 25 · see citing works
A Perceptual-Based Noise-Agnostic 3D Skeleton Motion Data Refinement Network
Li, S., Zhu, H., Zheng, L., & Li, L. (2020) · IEEE Access · doi:10.1109/access.2020.2980316 · Cited by 24 · see citing works
Categorical Codebook Matching for Embodied Character Controllers
Starke, S., Starke, P., He, N. S., Komura, T., & Ye, Y. (2024) · ACM Transactions on Graphics · doi:10.1145/3658209 · Cited by 23 · see citing works
An RNN-Ensemble approach for Real Time Human Pose Estimation from Sparse IMUs
Nagaraj, D., Schake, E., Leiner, P., & Werth, D. (2020) · Applications Intelligent Systems · doi:10.1145/3378184.3378228 · Cited by 23 · see citing works
Unsupervised behaviour analysis and magnification (uBAM) using deep learning
Brattoli, B., Büchler, U., Dorkenwald, M., Reiser, P., Filli, L., Helmchen, F., Wahl, A.-S., & Ommer, B. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00326-x · Cited by 21 · see citing works
A review of 3D human body pose estimation and mesh recovery
Muhammad, Z.-U.-D., Huang, Z., & Khan, R. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103628 · Cited by 23 · see citing works
Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket
Zuo, C., Wang, Y., Zhan, L., Guo, S., Yi, X., Xu, F., & Qin, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00215 · Cited by 22 · see citing works
Karim, M. F. A., Khalid, S., Lee, S., Almutairi, S. S., Namoun, A., & Abohashrh, M. (2025) · IEEE Access · doi:10.1109/access.2025.3590073 · Cited by 23 · see citing works
Chen, S., Bangaru, S. S., Yigit, T., Trkov, M., Wang, C., & Yi, J. (2021) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim46487.2021.9517592 · Cited by 22 · see citing works
Self-Calibrated Multi-Sensor Wearable for Hand Tracking and Modeling
Gosala, N., Wang, F., Cui, Z., Liang, H., Glauser, O., Wu, S., & Sorkine‐Hornung, O. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3131230 · Cited by 22 · see citing works
Mocap Everyone Everywhere: Lightweight Motion Capture with Smartwatches and a Head-Mounted Camera
Lee, J., & Joo, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00110 · Cited by 26 · see citing works
Zheng, Z., Ma, H., Yan, W., Liu, H., & Yang, Z. (2021) · Entropy · doi:10.3390/e23050588 · Cited by 21 · see citing works
HMD-NeMo: Online 3D Avatar Motion Generation From Sparse Observations
Aliakbarian, S., Saleh, F. S., Collier, D., Cameron, P., & Cosker, D. P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00882 · Cited by 21 · see citing works
Riding Motion Capture System Using Inertial Measurement Units with Contact Constraints
Maruyama, T., Tada, M., & Toda, H. (2019) · International Journal of Automation Technology · doi:10.20965/ijat.2019.p0506 · Cited by 21 · see citing works
Li, J., Zhu, K., Li, D., Kang, P., & Shull, P. B. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3349639 · Cited by 20 · see citing works
IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition
Konak, O., Wegner, P., & Bert Arnrich, A. (2020) · Sensors · doi:10.3390/s20247179 · Cited by 20 · see citing works
LiveHPS: LiDAR-Based Scene-Level Human Pose and Shape Estimation in Free Environment
Ren, Y., Han, X., Zhao, C., Wang, J., Xu, L., Jingyi Yu, A., & Ma, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00128 · Cited by 24 · see citing works
SMART-vision: survey of modern action recognition techniques in vision
AlShami, A. K., Rabinowitz, R., Lâm, K. N., Shleibik, Y., Mersha, M. A., Boult, T. E., & Kalita, J. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20484-5 · Cited by 20 · see citing works
Li, Y., Li, F. M., & Carrington, P. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580660 · Cited by 19 · see citing works
Physics-based Motion Retargeting from Sparse Inputs
Reda, D., Won, J., Ye, Y., van de Panne, M., & Winkler, A. (2023) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3606928 · Cited by 21 · see citing works
LiDARCapV2: 3D human pose estimation with human–object interaction from LiDAR point clouds
Zhang, J., Mao, Q., Shen, S., Wen, C., Xu, L., & Wang, C. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110848 · Cited by 17 · see citing works
Lisca, G., Prodaniuc, C., Grauschopf, T., & Axenie, C. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3094929 · Cited by 18 · see citing works
3D Human Body Pose Estimation in Virtual Reality: A survey
Anvari, T., & Park, K. (2022) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc55196.2022.9952586 · Cited by 17 · see citing works
Arbitrary Spatial Trajectory Reconstruction Based on a Single Inertial Sensor
Wang, Y., & Zhao, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3257867 · Cited by 17 · see citing works
- Authors
- Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., & Pons-Moll, G.
- Affiliations
- Max Planck Institute for Intelligent Systems (DE)
- ETH Zurich (CH)
- Max Planck Institute for Informatics, Saarbrücken, Germany
- Venue
- ACM Transactions on Graphics
- Published
- 2018-12-04
- Volume
- 37
- Issue
- 6
- Pages
- 1-15
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2898073175
- Cited by
- 342
- References
- 72
- Citations by year
- 2026: 43 · 2025: 54 · 2024: 69 · 2023: 56 · 2022: 38 · 2021: 44 · 2020: 26 · 2019: 12
- FWCI
- 10.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Graphics (publishedVersion)
- Funders
- Nvidia
- H2020 European Research Council (717054)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- inertial measurement unit, full-body pose estimation, real-time pose estimation, bidirectional recurrent neural network, motion capture, wearable sensors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
