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We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources.
Unsupervised learning of human motion models
Yang Song, Luis Goncalves, and Pietro Perona · 2001
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Decomposing biological motion: A framework for analysis and synthesis of human gait patterns
Nikolaus F. Troje · 2002
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Documentation mocap database HDM05
Meinard Müller, Tido Röder, Michael Clausen, Bernhard Eberhardt, Björn Krüger, and Andreas Weber · 2007
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Clustered pose and nonlinear appearance models for human pose estimation
Sam Johnson and Mark Everingham · 2010
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Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion
Leonid Sigal, Alexandru O Balan, and Michael J Black · 2010
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Sleight of hand: Perception of finger motion from reduced marker sets
Ludovic Hoyet, Kenneth Ryall, Rachel McDonnell, and Carol O’Sullivan · 2012
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Coupled action recognition and pose estimation from multiple views
Angela Yao, Juergen Gall, and Luc Gool · 2012
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An unsupervised approach for automatic activity recognition based on hidden markov model regression
Dorra Trabelsi, Samer Mohammed, Faicel Chamroukhi, Latifa Oukhellou, and Yacine Amirat · 2013
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An approach to pose-based action recognition
Chunyu Wang, Yizhou Wang, and Alan L. Yuille · 2013
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Human pose estimation: New benchmark and state of the art analysis
Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler, and Bernt Schiele · 2014
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Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Efficient non-linear markov models for human motion
Andreas Lehrmann, Peter V. Gehler, and Sebastian Nowozin · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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MoSh: Motion and Shape Capture from Sparse Markers
Matthew Loper, Naureen Mahmood, and Michael J. Black · 2014
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Mosh: Motion and shape capture from sparse markers
Matthew Loper, Naureen Mahmood, and Michael J Black · 2014
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Pose-conditioned joint angle limits for 3D human pose reconstruction
Ijaz Akhter and Michael J. Black · 2015
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Smpl: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2015
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The kit whole-body human motion database
Christian Mandery, Ömer Terlemez, Martin Do, Nikolaus Vahrenkamp, and Tamim Asfour · 2015
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Keep it smpl: Automatic estimation of 3d human pose and shape from a single image
Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J Black · 2016
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Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
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Ntu rgb+ d: A large scale dataset for 3d human activity analysis
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang · 2016
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Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks
Wentao Zhu, Cuiling Lan, Junliang Xing, Wenjun Zeng, Yanghao Li, Li Shen, and Xiaohui Xie · 2016
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Dynamic faust: Registering human bodies in motion
Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2017
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Realtime multi-person 2d pose estimation using part affinity fields
Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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Efficient unsupervised temporal segmentation of motion data
Björn Krüger, Anna Vögele, Tobias Willig, Angela Yao, Reinhard Klein, and Andreas Weber · 2017
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Pku-mmd: A large scale benchmark for continuous multi-modal human action understanding
Chunhui Liu, Yueyu Hu, Yanghao Li, Sijie Song, and Jiaying Liu · 2017
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Skeleton-based action recognition using spatio-temporal lstm network with trust gates
Jun Liu, Amir Shahroudy, Dong Xu, Alex C Kot, and Gang Wang · 2017
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Global context-aware attention lstm networks for 3d action recognition
Jun Liu, Gang Wang, Ping Hu, Ling-Yu Duan, and Alex C Kot · 2017
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A simple yet effective baseline for 3d human pose estimation
Julieta Martinez, Rayat Hossain, Javier Romero, and James J Little · 2017
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Monocular 3d human pose estimation in the wild using improved cnn supervision
Dushyant Mehta, Helge Rhodin, Dan Casas, Pascal Fua, Oleksandr Sotnychenko, Weipeng Xu, and Christian Theobalt · 2017
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Embodied hands: Modeling and capturing hands and bodies together
Javier Romero, Dimitrios Tzionas, and Michael J. Black · 2017
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An end-to-end spatio-temporal attention model for human action recognition from skeleton data
Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, and Jiaying Liu · 2017
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Total capture: 3d human pose estimation fusing video and inertial sensors
Matthew Trumble, Andrew Gilbert, Charles Malleson, Adrian Hilton, and John P. Collomosse · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Posetrack: A benchmark for human pose estimation and tracking
Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov, Leonid Pishchulin, Anton Milan, Juergen Gall, and Bernt Schiele · 2018
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Densepose: Dense human pose estimation in the wild
Rıza Alp Güler, Natalia Neverova, and Iasonas Kokkinos · 2018
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End-to-end recovery of human shape and pose
Angjoo Kanazawa, Michael J Black, David W Jacobs, and Jitendra Malik · 2018
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2d/3d pose estimation and action recognition using multitask deep learning
Diogo C. Luvizon, David Picard, and Hedi Tabia · 2018
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Ordinal depth supervision for 3d human pose estimation
Georgios Pavlakos, Xiaowei Zhou, and Kostas Daniilidis · 2018
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Integral human pose regression
Xiao Sun, Bin Xiao, Fangyin Wei, Shuang Liang, and Yichen Wei · 2018
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Recovering accurate 3d human pose in the wild using imus and a moving camera
Timo von Marcard, Roberto Henschel, Michael J Black, Bodo Rosenhahn, and Gerard Pons-Moll · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
Sijie Yan, Yuanjun Xiong, and Dahua Lin · 2018
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Digital dance ethnography: Organizing large dance collections
Andreas Aristidou, Ariel Shamir, and Yiorgos Chrysanthou · 2019
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Exploiting temporal context for 3d human pose estimation in the wild
Anurag Arnab, Carl Doersch, and Andrew Zisserman · 2019
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Exploiting spatial-temporal relationships for 3d pose estimation via graph convolutional networks
Yujun Cai, Liuhao Ge, Jun Liu, Jianfei Cai, Tat-Jen Cham, Junsong Yuan, and Nadia Magnenat Thalmann · 2019
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Poselifter: Absolute 3d human pose lifting network from a single noisy 2d human pose
Ju Yong Chang, Gyeongsik Moon, and Kyoung Mu Lee · 2019
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Weakly-supervised discovery of geometry-aware representation for 3d human pose estimation
Xipeng Chen, Kwan-Yee Lin, Wentao Liu, Chen Qian, and Liang Lin · 2019
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Channel-wise topology refinement graph convolution for skeleton-based action recognition
Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, and Weiming Hu · 2021
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Beyond static features for temporally consistent 3d human pose and shape from a video
Hongsuk Choi, Gyeongsik Moon, Ju Yong Chang, and Kyoung Mu Lee · 2021
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Exemplar fine-tuning for 3d human model fitting towards in-the-wild 3d human pose estimation
Hanbyul Joo, Natalia Neverova, and Andrea Vedaldi · 2021
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Pare: Part attention regressor for 3d human body estimation
Muhammed Kocabas, Chun-Hao P. Huang, Otmar Hilliges, and Michael J. Black · 2021
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Probabilistic modeling for human mesh recovery
Nikos Kolotouros, Georgios Pavlakos, Dinesh Jayaraman, and Kostas Daniilidis · 2021
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Hai Ci, Chunyu Wang, Xiaoxuan Ma, and Yizhou Wang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Learning 3d human dynamics from video
Angjoo Kanazawa, Jason Y Zhang, Panna Felsen, and Jitendra Malik · 2019
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Learning to reconstruct 3d human pose and shape via model-fitting in the loop
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Convolutional mesh regression for single-image human shape reconstruction
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Actional-structural graph convolutional networks for skeleton-based action recognition
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, and Qi Tian · 2019
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Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding
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Jiefeng Li, Chao Xu, Zhicun Chen, Siyuan Bian, Lixin Yang, and Cewu Lu · 2021
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3d human action representation learning via cross-view consistency pursuit
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One-shot action recognition in challenging therapy scenarios
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Self-supervised 3d skeleton action representation learning with motion consistency and continuity
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Action-guided 3d human motion prediction
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Monocular, one-stage, regression of multiple 3d people
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Skeleton-contrastive 3d action representation learning
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Encoder-decoder with multi-level attention for 3d human shape and pose estimation
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Probabilistic monocular 3d human pose estimation with normalizing flows
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Graph stacked hourglass networks for 3d human pose estimation
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Unik: A unified framework for real-world skeleton-based action recognition
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Skeleton cloud colorization for unsupervised 3d action representation learning
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Pymaf: 3d human pose and shape regression with pyramidal mesh alignment feedback loop
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3d human pose estimation with spatial and temporal transformers
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