Topologically-constrained latent variable models

Nonlinear Dimensionality Reduction by Locally Linear Embedding

Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965 · see citing works

A Global Geometric Framework for Nonlinear Dimensionality Reduction

Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works

Motion graphs

Kovar, L., Gleicher, M. L., & Pighin, F. P. (2008) · ACM SIGGRAPH Courses · doi:10.1145/1401132.1401202 · Cited by 1,073 · see citing works

Gaussian Process Dynamical Models for Human Motion

Wang, J. M., Fleet, D. J., & Hertzmann, A. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1167 · Cited by 1,024 · see citing works

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

Lawrence, N. D. (2005) · Cited by 906 · see citing works

Style-based inverse kinematics

Grochow, K., Martin, S. L., Hertzmann, A., & Popović, Z. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015755 · Cited by 650 · see citing works

Learning a kernel matrix for nonlinear dimensionality reduction

Weinberger, K. Q., Sha, F., & Saul, L. K. (2004) · doi:10.1145/1015330.1015345 · Cited by 526 · see citing works

3D People Tracking with Gaussian Process Dynamical Models

Urtasun, R., Fleet, D. J., & Fua, P. (2006) · 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.2006.15 · Cited by 444 · see citing works

Inferring 3D body pose from silhouettes using activity manifold learning

Elgammal, A., & Lee, C.-S. (2004) · 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.2004.1315230 · Cited by 421 · see citing works

Gaussian Process Dynamical Models

Wang, J., Hertzmann, A., & Blei, D. M. (2005) · Cited by 418 · see citing works

Local distance preservation in the GP-LVM through back constraints

Lawrence, N. D., & Quiñonero-Candela, J. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143909 · Cited by 219 · see citing works

Modeling and Learning Contact Dynamics in Human Motion

Bissacco, A. (2005) · 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.2005.225 · Cited by 30 · see citing works

Structural-RNN: Deep Learning on Spatio-Temporal Graphs

Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133 · see citing works

Recurrent Network Models for Human Dynamics

Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.494 · Cited by 904 · see citing works

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596 · see citing works

Adversarial Geometry-Aware Human Motion Prediction

Gui, L.-Y., Wang, Y.-X., Liang, X., & Moura, J. M. F. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_48 · Cited by 276 · see citing works

Long-Term Human Motion Prediction with Scene Context

Cao, Z., Gao, H., Mangalam, K., Cai, Q.-Z., Vo, M., & Malik, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_23 · Cited by 227 · see citing works

Continuous character control with low-dimensional embeddings

Levine, S., Wang, J. M., Haraux, A., Popović, Z., & Koltun, V. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185524 · Cited by 170 · see citing works

Dynamical binary latent variable models for 3D human pose tracking

Taylor, G. W., Sigal, L., Fleet, D. J., & Hinton, G. E. (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.2010.5540157 · Cited by 140 · see citing works

Few-Shot Human Motion Prediction via Meta-learning

Gui, L.-Y., Wang, Y.-X., Ramanan, D., & Moura, J. M. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_27 · Cited by 125 · see citing works

Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video

Liu, M., Tang, S., Li, Y., & Rehg, J. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_41 · Cited by 125 · see citing works

Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes

Wang, J., Xu, H., Xu, J., Liu, S., & Wang, X. (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/cvpr46437.2021.00928 · Cited by 122 · see citing works

Two Distributed-State Models For Generating High-Dimensional Time Series

Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2011) · doi:10.5555/1953048.2021035 · Cited by 114 · see citing works

Structured Time Series Analysis for Human Action Segmentation and Recognition

Gong, D., Medioni, G. G., & Zhao, X. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.244 · Cited by 109 · see citing works

Factorized Orthogonal Latent Spaces

Salzmann, M., Ek, C. H., Urtasun, R., & Darrell, T. (2010) · Cited by 100 · see citing works

Predicting 3D Human Dynamics From Video

Zhang, J. Y., Felsen, P., Kanazawa, A., & Malik, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00721 · Cited by 93 · see citing works

A constrained latent variable model

Varol, A., Salzmann, M., Fua, P., & Urtasun, R. (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.2012.6247934 · Cited by 85 · see citing works

Dynamic Manifold Warping for view invariant action recognition

Gong, D., & Medioni, G. G. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126290 · Cited by 83 · see citing works

Fully Accounting for English Learner Performance

Hopkins, M., Thompson, K. D., Linquanti, R., Hakuta, K., & August, D. L. (2013) · Educational Researcher · doi:10.3102/0013189x12471426 · Cited by 77 · see citing works

Eyecatch

Yeo, S.-H., Lesmana, M., Neog, D. R., & Pai, D. K. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185538 · Cited by 65 · see citing works

3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers

Li, R., Tian, T.-P., Sclaroff, S., & Yang, M. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0283-4 · Cited by 61 · see citing works

Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling

Ding, M., & Fan, G. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2373393 · 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

Discriminative vision-based recovery and recognition of human motion

Poppe, R. W. (2009) · doi:10.3990/1.9789036528108 · Cited by 54 · see citing works

Efficient convolutional hierarchical autoencoder for human motion prediction

Li, Y., Wang, Z., Yang, X., Wang, M., Poiana, S. I., Chaudhry, E., & Zhang, J. (2019) · The Visual Computer · doi:10.1007/s00371-019-01692-9 · Cited by 56 · see citing works

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Wang, Z., Yu, P., Zhao, Y., Zhang, R., Zhou, Y., Yuan, J., & Chen, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6911 · Cited by 52 · see citing works

Latent spaces for dynamic movement primitives

Bitzer, S., & Vijayakumar, S. (2009) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2009.5379530 · Cited by 49 · see citing works

Efficient movement representation by embedding Dynamic Movement Primitives in deep autoencoders

Chen, N., Bayer, J., Urban, S., & van der Smagt, P. (2015) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2015.7363570 · Cited by 49 · see citing works

Multimodal Similarity Gaussian Process Latent Variable Model

Song, G., Wang, S., Huang, Q., & Tian, Q. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713045 · Cited by 47 · see citing works

Structure-Aware Human-Action Generation

Yu, P., Zhao, Y., Li, C., Yuan, J., & Chen, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_2 · Cited by 46 · see citing works

Robust Multiple Manifolds Structure LearningPreprint

Gong, D., Zhao, X., & Medioni, G. G. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4624 · Cited by 42 · see citing works

Spatio-temporal modeling of grasping actions

Romero, J., Feix, T., Kjellström, H., & Kragić, D. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5650701 · Cited by 41 · see citing works

Rank priors for continuous non-linear dimensionality reduction

Geiger, A., Urtasun, R., & Darrell, T. (2009) · 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.2009.5206672 · Cited by 32 · see citing works

Harmonized Multimodal Learning with Gaussian Process Latent Variable Models

Song, G., Wang, S., Huang, Q., & Tian, Q. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2942028 · Cited by 32 · see citing works

Shared Gaussian Process Latent Variables Models

Ek, C. H. (2009) · Cited by 32 · see citing works

Gaussian Process Latent Random Field

Zhong, G., Li, W.-J., Yeung, D., Hou, X., & Liu, C. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7697 · Cited by 29 · see citing works

Structural Laplacian Eigenmaps for Modeling Sets of Multivariate Sequences

Lewandowski, M., Makris, D., Velastín, S. A., & Nebel, J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2277664 · Cited by 29 · see citing works

Data-Free Prior Model for Upper Body Pose Estimation and Tracking

Chen, J., Nie, S., & Ji, Q. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2274748 · Cited by 28 · see citing works

Sparse Coding for Learning Interpretable Spatio-Temporal Primitives

Kim, T.-H., Shakhnarovich, G., & Urtasun, R. (2010) · Cited by 26 · see citing works

Dynamical Simulation Priors for Human Motion Tracking

Vondrak, M., Sigal, L., & Jenkins, O. C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.61 · Cited by 25 · see citing works

Hierarchical Style-Based Networks for Motion Synthesis

Xu, J., Xu, H., Ni, B., Yang, X., Wang, X., & Darrell, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_11 · Cited by 24 · see citing works

Incorporating Physics Principles for Precise Human Motion Prediction

Zhang, Y., Kephart, J. O., & Ji, Q. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00605 · Cited by 24 · see citing works

Learning representations from multiple manifolds

Lee, C.-S., Elgammal, A., & Torki, M. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.024 · Cited by 24 · see citing works

Video-Based Human Walking Estimation Using Joint Gait and Pose Manifolds

Zhang, X., Ding, M., & Fan, G. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2527218 · Cited by 22 · see citing works

Skeleton-Based Human Motion Prediction With Privileged Supervision

Dong, M., & Xu, C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3166861 · Cited by 22 · see citing works

Sufficient dimension reduction for visual sequence classification

Shyr, A., Urtasun, R., & Jordan, M. I. (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.2010.5539922 · Cited by 22 · see citing works

Gaussian process motion graph models for smooth transitions among multiple actions

Ukita, N., & Takeo Kanade, A. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2011.11.005 · Cited by 21 · see citing works

Unsupervised hierarchical modeling of locomotion styles

Pan, W., & Torresani, L. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553475 · Cited by 20 · see citing works

Recurrent Network Models for Kinematic Tracking.Preprint

Fragkiadaki, K., Levine, S., & Malik, J. (2015) · arXiv (Cornell University) · Cited by 19 · see citing works

Forecasting Human Object Interaction: Joint Prediction of Motor Attention and Egocentric Activity.

Liu, M., Tang, S., Li, Y., & Rehg, J. M. (2019) · Cited by 18 · see citing works

Divide, Conquer and Coordinate: Globally Coordinated Switching Linear Dynamical System

Li, R., Tian, T.-P., & Sclaroff, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.152 · Cited by 17 · see citing works

Manifold Learning for Object Tracking with Multiple Motion Dynamics

Nascimento, J. C., & Silva, J. G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_13 · Cited by 17 · see citing works

Predicting Articulated Human Motion from Spatial Processes

Hauberg, S., & Pedersen, K. S. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0433-3 · Cited by 16 · see citing works

Tracking human pose with multiple activity models

Darby, J., Li, B., & Costen, N. P. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.03.018 · Cited by 16 · see citing works

Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set

Gong, J., Fan, G., Yu, L., Havlicek, J., Chen, D., & Fan, N. (2014) · Sensors · doi:10.3390/s140610124 · Cited by 15 · see citing works

Joint view-identity manifold for infrared target tracking and recognition

Gong, J., Fan, G., Yu, L., Havlicek, J., Chen, D., & Fan, N. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.10.002 · Cited by 15 · see citing works

Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models

Ha, J.-S., Chae, H.-J., & Han-lim Choi, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2856915 · Cited by 15 · see citing works

Principal Curve Algorithms for Partitioning High-Dimensional Data Spaces

Zhang, J., Wang, X., Krüger, U., & Wang, F. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2100408 · Cited by 14 · see citing works

Computationally Efficient Rigid-Body Gaussian Process for Motion Dynamics

Lang, M., & Hirche, S. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2677469 · Cited by 14 · see citing works

Multi-agent Transformer Networks for Multimodal Human Activity Recognition

Li, J., Yao, L., Li, B., Wang, X., & Sammut, C. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557402 · Cited by 12 · see citing works

Data‐Driven Reconstruction of Human Locomotion Using a Single Smartphone

Eom, H., Choi, B., & Noh, J. (2014) · Computer Graphics Forum · doi:10.1111/cgf.12469 · Cited by 12 · see citing works

Motion Synthesis through Randomized Exploration on Submanifolds of Configuration Space

Havoutis, I., & Ramamoorthy, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11876-0_9 · Cited by 11 · see citing works

Observable subspaces for 3D human motion recovery

Fossati, A., Salzmann, M., & Fua, P. (2009) · 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.2009.5206489 · Cited by 12 · see citing works

Quantitative Analysis of Nonlinear Embedding

Zhang, J., Wang, Q., He, L., & Zhou, Z. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2171991 · Cited by 11 · see citing works

Motion Models for People Tracking

Fleet, D. J. (2011) · Springer eBooks · doi:10.1007/978-0-85729-997-0_10 · Cited by 11 · see citing works

Gaussian process for 6-DoF rigid motions

Lang, M., Kleinsteuber, M., & Hirche, S. (2017) · Autonomous Robots · doi:10.1007/s10514-017-9683-4 · Cited by 11 · see citing works

Multiactivity 3-D Human Pose Tracking in Incorporated Motion Model with Transition Bridges

Yu, J., & Sun, J. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2664891 · Cited by 10 · see citing works

Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer’s Disease

Gunawardena, R., Sarrigiannis, P. G., Blackburn, D. J., & He, F. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.05.033 · Cited by 10 · see citing works

Feature Extraction of Constrained Dynamic Latent Variables

Ma, Y., Zhao, S., & Huang, B. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2901934 · Cited by 10 · see citing works

Supervised Spectral Latent Variable Models

Bo, L., & Sminchisescu, C. (2009) · Cited by 10 · see citing works

Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes

Mullen, J. F., Kothandaraman, D., Bera, A., & Manocha, D. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00038 · Cited by 9 · see citing works

A novel locomotion synthesis and optimisation framework for insects

Guo, S., Chang, J. M., Cao, Y., & Zhang, J. (2013) · Computers & Graphics · doi:10.1016/j.cag.2013.10.021 · Cited by 9 · see citing works

Distilled mid-fusion transformer networks for multi-modal human activity recognition

Li, J., Yao, L., Li, B., & Sammut, C. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114160 · Cited by 9 · see citing works

Manifold Learning for Object Tracking With Multiple Nonlinear Models

Nascimento, J. C., Silva, J. G., Marques, J. S., & Lemos, J. M. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2303652 · Cited by 9 · see citing works

Bayesian filtering with online Gaussian process latent variable models

Wang, Y., Brubaker, M. A., Chaib-draa, B., & Urtasun, R. (2014) · Cited by 9 · see citing works

On Retrospecting Human Dynamics with Attention

Dong, M., & Xu, C. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/100 · Cited by 8 · see citing works

Simultaneous particle tracking in multi-action motion models with synthesized paths

Ukita, N. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.09.010 · Cited by 8 · see citing works

Multi-layer joint gait-pose manifold for human motion modeling

Ding, M., & Fan, G. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553783 · Cited by 8 · see citing works

Gaussian process for human motion modeling: A comparative study

Fan, G., Zhang, X., & Ding, M. (2011) · Machine learning for signal processing ... · doi:10.1109/mlsp.2011.6064631 · Cited by 8 · see citing works

Joint gait-pose manifold for video-based human motion estimation

Zhang, X., & Fan, G. (2011) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2011.5981795 · Cited by 8 · see citing works

Tracking hand rotation and various grasping gestures from an IR camera using extended cylindrical manifold embedding

Lee, C.-S., Chun, S., & Park, S. W. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.08.006 · Cited by 7 · see citing works

Joint view-identity manifold for target tracking and recognition

Gong, J., Fan, G., Yu, L., Havlicek, J., & Chen, D. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467120 · Cited by 7 · see citing works

Feature-Based Pose Estimation

Sminchisescu, C., Bo, L., Ionescu, C., & Kanaujia, A. (2011) · Springer eBooks · doi:10.1007/978-0-85729-997-0_12 · Cited by 7 · see citing works

Rank priors for continuous non-linear dimensionality reduction

Geiger, A., Urtasun, R., & Darrell, T. (2009) · 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/cvprw.2009.5206672 · Cited by 6 · see citing works

Probabilistic Discriminative Dimensionality Reduction for Pose-Based Action Recognition

Ntouskos, V., Papadakis, P., & Pirri, F. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-12610-4_9 · Cited by 5 · see citing works

Structure-guided manifold learning for video-based motion estimation

Ding, M., Fan, G., Zhang, X., Song, G., & Chou, L. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467275 · Cited by 5 · see citing works

Multi-manifold discriminant local spline embedding

He, P., Xu, X., Chang, X., Ding, J., & Chen, S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108714 · Cited by 5 · see citing works

Multi-activity 3D human motion recognition and tracking in composite motion model with synthesized transition bridges

Yu, J., Sun, J., Liu, S., & Luo, S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4847-y · Cited by 5 · see citing works

Video analysis based on Multi-Kernel Representation with automatic parameter choice

Álvarez-Meza, A. M., Valencia-Aguirre, J., Daza-Santacoloma, G., Acosta-Medina, C. D., & Castellanos-Dominguez, G. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.10.034 · Cited by 4 · see citing works

Continuous Action Recognition by Action-specific Motion Models

Morimoto, K., Matsuyama, Y., & Ukita, N. (2013) · Machine Vision and Applications · Cited by 4 · see citing works

Revisiting Gaussian process dynamical models

Zhao, J., & Sun, S. (2015) · International Conference on Artificial Intelligence · Cited by 4 · see citing works

Discriminative Sequence Back-constrained GP-LVM for MOCAP based Action Recognition

Ntouskos, V., Papadakis, P., & Pirri, F. (2013) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0004268600870096 · Cited by 4 · see citing works

PACE: Data-Driven Virtual Agent Interaction in Dense and Cluttered Environments

Mullen, J. F., & Manocha, D. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3247054 · Cited by 4 · see citing works

Exploring affordances in robot grasping through latent structure representation

Ek, C. H., Song, D., Huebner, K., & Kragić, D. (2010) · Bristol Research (University of Bristol) · Cited by 4 · see citing works

Kick-starting GPLVM optimization via a connection to metric MDS

Bitzer, S., & Williams, C. K. I. (2010) · Max Planck Digital Library · Cited by 4 · see citing works

Sparse summarization of robotic grasping data

Hjelm, M., Ek, C. H., Detry, R. J., Kjellström, H., & Kragić, D. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630707 · Cited by 3 · see citing works

Conditional neural relational inference for interacting systems

Ramos, J. A. C., Blondé, L., Armand, S., & Kalousis, A. (2021) · ArODES (HES-SO (https://www.hes-so.ch/)) · Cited by 3 · see citing works

A Manifold Representation as Common Basis for Action Production and Recognition

Steffen, J., Pardowitz, M., & Ritter, H. J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04617-9_76 · Cited by 3 · see citing works

Clustering-Based Latent Variable Models for Monocular Non-rigid 3D Shape Recovery

Wang, Q., Wang, F., Li, D., & Wang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09339-0_17 · Cited by 3 · see citing works

Infrared target tracking, recognition and segmentation using shape-aware level set

Gong, J., Fan, G., Havlicek, J., Fan, N., & Chen, D. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738676 · Cited by 3 · see citing works

Action-guided 3D Human Motion Prediction

Sun, J., Lin, Z., Han, X., Hu, J., Xu, J., & Zheng, W. (2021) · neural information processing systems · Cited by 3 · see citing works

Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian ProcessPreprint

Wang, Y., & Dunson, D. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03768 · Cited by 3 · see citing works

Modeling Human Locomotion with Topologically Constrained Latent Variable Models

Urtasun, R., Fleet, D. J., & Lawrence, N. D. (2007) · Springer eBooks · doi:10.1007/978-3-540-75703-0_8 · Cited by 32 · see citing works

Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifoldsPreprint

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