21.4.1.1 Human Posture, or Human Pose, Learning, Neural Networks

Chapter Contents (Back)
Human Pose. Human Posture. Body Pose. Posture. Pose. Neural Networks. CNN. Learning.

Pourdamghani, N.[Nima], Rabiee, H.R.[Hamid R.], Faghri, F.[Fartash], Rohban, M.H.[Mohammad Hossein],
Graph based semi-supervised human pose estimation: When the output space comes to help,
PRL(33), No. 12, 1 September 2012, pp. 1529-1535.
Elsevier DOI 1208
Human pose estimation; Graph based; Semi-supervised; Manifold regularization BibRef

Pourdamghani, N.[Nima], Rabiee, H.R.[Hamid R.], Zolfaghari, M.[Mohammadreza],
Metric learning for graph based semi-supervised human pose estimation,
ICPR12(3386-3389).
WWW Link. 1302
BibRef

Li, S.J.[Si-Jin], Liu, Z.Q.[Zhi-Qiang], Chan, A.B.[Antoni B.],
Heterogeneous Multi-Task Learning for Human Pose Estimation with Deep Convolutional Neural Network,
IJCV(113), No. 1, May 2015, pp. 19-36.
Springer DOI 1506
BibRef
Earlier: DeepLearn14(488-495)
IEEE DOI 1409
BibRef
And: A1, A3, Only:
3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network,
ACCV14(II: 332-347).
Springer DOI 1504
deep learning; human pose estimation; multi-task learning BibRef

Unzueta, L., Aranjuelo, N., Goenetxea, J., Rodriguez, M., Linaza, M.T.,
Contextualised learning-free three-dimensional body pose estimation from two-dimensional body features in monocular images,
IET-CV(10), No. 4, 2016, pp. 299-306.
DOI Link 1608
cameras BibRef

Gouiaa, R.[Rafik], Meunier, J.[Jean],
Learning cast shadow appearance for human posture recognition,
PRL(97), No. 1, 2017, pp. 54-60.
Elsevier DOI 1709
Cast shadows BibRef

Jammalamadaka, N.[Nataraj], Zisserman, A.[Andrew], Jawahar, C.V.,
Human pose search using deep networks,
IVC(59), No. 1, 2017, pp. 31-43.
Elsevier DOI 1704
BibRef
Earlier:
Human pose search using deep poselets,
FG15(1-8)
IEEE DOI 1508
Pose retrieval. image representation BibRef

Ning, G., Zhang, Z., He, Z.,
Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation,
MultMed(20), No. 5, May 2018, pp. 1246-1259.
IEEE DOI 1805
Biological system modeling, Fractals, Knowledge engineering, Neural networks, Pose estimation, Training, Human pose estimation, knowledge-guided learning BibRef

Lim, J.[Jongin], Yoo, Y.J.[Young-Joon], Heo, B.[Byeongho], Choi, J.Y.[Jin Young],
Pose transforming network: Learning to disentangle human posture in variational auto-encoded latent space,
PRL(112), 2018, pp. 91-97.
Elsevier DOI 1809
Human pose transform, Disentangle hidden factors in latent space, Generative model, Variational auto-encoder BibRef

Wang, S., Xin, Y., Kong, D., Yin, B.,
Unsupervised Learning of Human Pose Distance Metric via Sparsity Locality Preserving Projections,
MultMed(21), No. 2, February 2019, pp. 314-327.
IEEE DOI 1902
Measurement, Robots, Resource management, Image recognition, Databases, Learning systems, Skeleton, Pose similarity, locality preserving projection BibRef

Liu, J.[Jian], Rahmani, H.[Hossein], Akhtar, N.[Naveed], Mian, A.S.[Ajmal S.],
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition,
IJCV(127), No. 10, October 2019, pp. 1545-1564.
Springer DOI 1909
BibRef

Nie, X., Feng, J., Xing, J., Xiao, S., Yan, S.,
Hierarchical Contextual Refinement Networks for Human Pose Estimation,
IP(28), No. 2, February 2019, pp. 924-936.
IEEE DOI 1811
Pose estimation, Complexity theory, Heating systems, Biological system modeling, Predictive models, Iterative methods, hierarchical contextual refinement network BibRef

Cha, G.[Geonho], Lee, M.[Minsik], Cho, J.C.[Jung-Chan], Oh, S.H.[Song-Hwai],
Deep pose consensus networks,
CVIU(182), 2019, pp. 64-70.
Elsevier DOI 1905
3D human pose estimation, Single-image-based 3D human pose estimation, Multiple-partial-hypothesis-based scheme. BibRef

Wu, Z.H.[Zhong-Hua], Lin, G.S.[Guo-Sheng], Cai, J.F.[Jian-Fei],
Keypoint based weakly supervised human parsing,
IVC(91), 2019, pp. 103801.
Elsevier DOI 1912
Human parsing, Weakly supervise, Iterative refinement, Keypoint, Skeleton, Correlation network BibRef

Chen, Y.C.[Yu-Cheng], Tian, Y.[Yingli], He, M.Y.[Ming-Yi],
Monocular human pose estimation: A survey of deep learning-based methods,
CVIU(192), 2020, pp. 102897.
Elsevier DOI 2002
Deep learning, Human pose estimation, Survey BibRef

Zheng, X.T.[Xiang-Tao], Chen, X.M.[Xiu-Mei], Lu, X.Q.[Xiao-Qiang],
A Joint Relationship Aware Neural Network for Single-Image 3D Human Pose Estimation,
IP(29), 2020, pp. 4747-4758.
IEEE DOI 2003
Pose estimation, Feature extraction, dual attention module BibRef

Wang, K.[Keze], Lin, L.[Liang], Jiang, C.H.[Chen-Han], Qian, C.[Chen], Wei, P.X.[Peng-Xu],
3D Human Pose Machines with Self-Supervised Learning,
PAMI(42), No. 5, May 2020, pp. 1069-1082.
IEEE DOI 2004
Pose estimation, Solid modeling, Task analysis, Deep learning, geometric deep learning BibRef

de Bem, R.[Rodrigo], Ghosh, A.[Arnab], Ajanthan, T.[Thalaiyasingam], Miksik, O.[Ondrej], Boukhayma, A.[Adnane], Siddharth, N., Torr, P.H.S.[Philip H.S.],
DGPose: Deep Generative Models for Human Body Analysis,
IJCV(128), No. 5, May 2020, pp. 1537-1563.
Springer DOI 2005
BibRef
Earlier: A1, A2, A3, A4, A6, A7, Only:
A Semi-supervised Deep Generative Model for Human Body Analysis,
HBU18(II:500-517).
Springer DOI 1905
BibRef

Bin, Y.R.[Yan-Rui], Chen, Z.M.[Zhao-Min], Wei, X.S.[Xiu-Shen], Chen, X.[Xinya], Gao, C.X.[Chang-Xin], Sang, N.[Nong],
Structure-aware human pose estimation with graph convolutional networks,
PR(106), 2020, pp. 107410.
Elsevier DOI 2006
Human pose estimation, Graph convolutional networks, Key points structural relations BibRef

Rogez, G.[Grégory], Schmid, C.[Cordelia],
Image-Based Synthesis for Deep 3D Human Pose Estimation,
IJCV(126), No. 9, September 2018, pp. 993-1008.
Springer DOI 1809
BibRef

Rogez, G.[Grégory], Weinzaepfel, P.[Philippe], Schmid, C.[Cordelia],
LCR-Net++: Multi-Person 2D and 3D Pose Detection in Natural Images,
PAMI(42), No. 5, May 2020, pp. 1146-1161.
IEEE DOI 2004
BibRef
Earlier:
LCR-Net: Localization-Classification-Regression for Human Pose,
CVPR17(1216-1224)
IEEE DOI 1711
Pose estimation, Proposals, Joints, Heating systems, Training data, CNN. Computer architecture, Standards, Training. BibRef

Leroy, V., Weinzaepfel, P.[Philippe], Brégier, R., Combaluzier, H., Rogez, G.[Grégory],
SMPLy Benchmarking 3D Human Pose Estimation in the Wild,
3DV20(301-310)
IEEE DOI 2102
Pose estimation, Benchmark testing, Videos, Shape, Pipelines, dataset BibRef

Sridhar Raj, S., Prasad, M.V.N.K.[Munaga V.N.K.], Balakrishnan, R.[Ramadoss],
Deep manifold clustering based optimal pseudo pose representation (DMC-OPPR) for unsupervised person re-identification,
IVC(101), 2020, pp. 103956.
Elsevier DOI 2009
Person re-identification, Clustering, Pose estimation, Representation, Deep learning BibRef

Tian, L.[Lei], Wang, P.[Peng], Liang, G.Q.[Guo-Qiang], Shen, C.H.[Chun-Hua],
An adversarial human pose estimation network injected with graph structure,
PR(115), 2021, pp. 107863.
Elsevier DOI 2104
Human pose estimation, Cascade feature network, Graph structure network, Generative adversarial network BibRef

Chen, Y.[Yu], Shen, C.H.[Chun-Hua], Wei, X.S.[Xiu-Shen], Liu, L.Q.[Ling-Qiao], Yang, J.[Jian],
Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation,
ICCV17(1221-1230)
IEEE DOI 1802
feedforward neural nets, learning (artificial intelligence), pose estimation, Adversarial PoseNet, Training BibRef

Gochoo, M.[Munkhjargal], Akhter, I.[Israr], Jalal, A.[Ahmad], Kim, K.[Kibum],
Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Kamel, A.[Aouaidjia], Sheng, B.[Bin], Li, P.[Ping], Kim, J.M.[Jin-Man], Feng, D.D.[David Dagan],
Hybrid Refinement-Correction Heatmaps for Human Pose Estimation,
MultMed(23), 2021, pp. 1330-1342.
IEEE DOI 2105
Pose estimation, Heating systems, Feature extraction, Predictive models, Convolutional neural networks, Detectors, heatmaps fusion BibRef

Nie, Q.A.[Qi-Ang], Liu, Y.H.[Yun-Hui],
View Transfer on Human Skeleton Pose: Automatically Disentangle the View-Variant and View-Invariant Information for Pose Representation Learning,
IJCV(129), No. 1, January 2021, pp. 1-22.
Springer DOI 2101
BibRef

Nie, Q.A.[Qi-Ang], Liu, Z.[Ziwei], Liu, Y.H.[Yun-Hui],
Unsupervised 3d Human Pose Representation with Viewpoint and Pose Disentanglement,
ECCV20(XIX:102-118).
Springer DOI 2011
BibRef

Benzine, A.[Abdallah], Luvison, B.[Bertrand], Pham, Q.C.[Quoc Cuong], Achard, C.[Catherine],
Single-shot 3D multi-person pose estimation in complex images,
PR(112), 2021, pp. 107534.
Elsevier DOI 2102
BibRef
Earlier:
Deep, Robust and Single Shot 3D Multi-Person Human Pose Estimation from Monocular Images,
ICIP19(584-588)
IEEE DOI 1910
Multi-person, 3D, Human pose, Deep learning BibRef

Benzine, A.[Abdallah], Chabot, F., Luvison, B.[Bertrand], Pham, Q.C.[Quoc Cuong], Achard, C.[Catherine],
PandaNet: Anchor-Based Single-Shot Multi-Person 3D Pose Estimation,
CVPR20(6855-6864)
IEEE DOI 2008
Pose estimation, Heating systems, Image resolution, Solid modeling, Skeleton. BibRef

Lin, K., Wang, L., Luo, K., Chen, Y., Liu, Z., Sun, M.T.,
Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation,
CirSysVideo(31), No. 3, March 2021, pp. 1066-1078.
IEEE DOI 2103
Training, Labeling, Task analysis, Skeleton, Image segmentation, Pose estimation, Feature extraction, Human parsing, domain adaptation BibRef

Zhao, L.[Lin], Wang, N.N.[Nan-Nan], Gong, C.[Chen], Yang, J.[Jian], Gao, X.[Xinbo],
Estimating Human Pose Efficiently by Parallel Pyramid Networks,
IP(30), 2021, pp. 6785-6800.
IEEE DOI 2108
Semantics, Pose estimation, Computational modeling, Heating systems, Task analysis, Graphical models, human pose estimation BibRef

Wang, H.[Hao], Luo, D.[Dingli], Ikenaga, T.[Takeshi],
Image Information Assistance Neural Network for VideoPose3D-based Monocular 3D Pose Estimation,
MVA21(1-4)
DOI Link 2109
Human computer interaction, Pose estimation, Neural networks, Cameras BibRef

Wang, J.D.[Jing-Dong], Sun, K.[Ke], Cheng, T.H.[Tian-Heng], Jiang, B.[Borui], Deng, C.R.[Chao-Rui], Zhao, Y.[Yang], Liu, D.[Dong], Mu, Y.D.[Ya-Dong], Tan, M.K.[Ming-Kui], Wang, X.G.[Xing-Gang], Liu, W.Y.[Wen-Yu], Xiao, B.[Bin],
Deep High-Resolution Representation Learning for Visual Recognition,
PAMI(43), No. 10, October 2021, pp. 3349-3364.
IEEE DOI 2109
Spatial resolution, Semantics, Object detection, Pose estimation, Convolutional codes, Indexes, Image segmentation, HRNet, object detection BibRef

Sun, K.[Ke], Xiao, B.[Bin], Liu, D.[Dong], Wang, J.D.[Jing-Dong],
Deep High-Resolution Representation Learning for Human Pose Estimation,
CVPR19(5686-5696).
IEEE DOI 2002
BibRef

Ben Gamra, M.[Miniar], Akhloufi, M.A.[Moulay A.],
A review of deep learning techniques for 2D and 3D human pose estimation,
IVC(114), 2021, pp. 104282.
Elsevier DOI 2109
2D and 3D human pose estimation, Single-person and multi-person pose estimation, BibRef

Fan, Z.[Zhen], Li, X.[Xiu], Li, Y.[Yipeng],
Multi-Agent Deep Reinforcement Learning for Online 3D Human Poses Estimation,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef


Mihajlovic, M.[Marko], Zhang, Y.[Yan], Black, M.J.[Michael J.], Tang, S.[Siyu],
LEAP: Learning Articulated Occupancy of People,
CVPR21(10456-10466)
IEEE DOI 2111
Deformable models, Solid modeling, Shape, Biological system modeling, NASA, Estimation BibRef

Liu, Z.G.[Zhen-Guang], Chen, H.M.[Hao-Ming], Feng, R.Y.[Run-Yang], Wu, S.[Shuang], Ji, S.L.[Shou-Ling], Yang, B.L.[Bai-Lin], Wang, X.[Xun],
Deep Dual Consecutive Network for Human Pose Estimation,
CVPR21(525-534)
IEEE DOI 2111
Recurrent neural networks, Corporate acquisitions, Pose estimation, Video sequences, Refining, Detectors BibRef

Tran, T.Q.[Trung Q.], Nguyen, G.V.[Giang V.], Kim, D.[Daeyoung],
Simple Multi-Resolution Representation Learning for Human Pose Estimation,
ICPR21(511-518)
IEEE DOI 2105
Heating systems, Image segmentation, Image resolution, Image recognition, Surveillance, Pose estimation, Computer architecture BibRef

Tripathi, S., Ranade, S., Tyagi, A., Agrawal, A.,
PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation,
3DV20(311-321)
IEEE DOI 2102
Training, Solid modeling, Skeleton, Predictive models, Semantics, 3DPW BibRef

Zheng, C.[Ce], Wu, W.H.[Wen-Han], Yang, T.J.[Tao-Jiannan], Zhu, S.J.[Si-Jie], Chen, C.[Chen], Liu, R.X.[Rui-Xu], Shen, J.[Ju], Kehtarnavaz, N.[Nasser], Shah, M.[Mubarak],
Deep Learning-Based Human Pose Estimation,
OnlineJanuary 2021.
PDF File. 2101
BibRef

Pramerdorfer, C., Strohmayer, J., Kampel, M.,
SDT: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification,
ICIP20(1611-1615)
IEEE DOI 2011
Thermal sensors, Image sensors, Sensor phenomena and characterization, deep learning BibRef

Dani, M.[Meghal], Narain, K.[Karan], Hebbalaguppe, R.[Ramya],
3DPoseLite: A Compact 3D Pose Estimation Using Node Embeddings,
WACV21(1877-1886)
IEEE DOI 2106
Solid modeling, Computational modeling, Pose estimation, Neural networks BibRef

Zhang, Y.H.[Ya-Hui], You, S.[Shaodi], Gevers, T.[Theo],
Orthographic Projection Linear Regression for Single Image 3D Human Pose Estimation,
ICPR21(8109-8116)
IEEE DOI 2105
Linear regression, Pose estimation, Neural networks, Imaging, Pattern recognition BibRef

Makantasis, K.[Konstantinos], Voulodimos, A.[Athanasios], Doulamis, A.[Anastasios], Bakalos, N.[Nikolaos], Doulamis, N.[Nikolaos],
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification,
ICPR21(4688-4695)
IEEE DOI 2105
Training, Solid modeling, Protocols, Tensors, Neural networks, Data models BibRef

Liu, H.[Hong], Guan, L.[Lisi],
Efficient High-Resolution High-Level-Semantic Representation Learning for Human Pose Estimation,
ICPR21(7862-7867)
IEEE DOI 2105
Convolution, Semantics, Pose estimation, Benchmark testing, Feature extraction, Computational efficiency, Data mining BibRef

Qammaz, A.[Ammar], Argyros, A.[Antonis],
Occlusion-tolerant and personalized 3D human pose estimation in RGB images,
ICPR21(6904-6911)
IEEE DOI 2105
Pose estimation, Neural networks, Kinematics, Color, Real-time systems, Pattern recognition BibRef

Bulat, A., Kossaifi, J., Tzimiropoulos, G., Pantic, M.,
Toward fast and accurate human pose estimation via soft-gated skip connections,
FG20(8-15)
IEEE DOI 2102
Pose estimation, Computer architecture, Neural networks, Training, Network architecture, Convolution, Logic gates, Convolutional Neural Networks BibRef

Zhou, L.[Lu], Chen, Y.Y.[Ying-Ying], Gao, Y.Z.[Yun-Ze], Wang, J.Q.[Jin-Qiao], Lu, H.Q.[Han-Qing],
Occlusion-aware Siamese Network for Human Pose Estimation,
ECCV20(XX:396-412).
Springer DOI 2011
BibRef

Xu, X.Y.[Xiang-Yu], Chen, H.[Hao], Moreno-Noguer, F.[Francesc], Jeni, L.A.[László A.], de la Torre, F.[Fernando],
3d Human Shape and Pose from a Single Low-resolution Image with Self-supervised Learning,
ECCV20(IX:284-300).
Springer DOI 2011
BibRef

Wang, J.[Jian], Long, X.[Xiang], Gao, Y.[Yuan], Ding, E.R.[Er-Rui], Wen, S.L.[Shi-Lei],
Graph-pcnn: Two Stage Human Pose Estimation with Graph Pose Refinement,
ECCV20(XI:492-508).
Springer DOI 2011
BibRef

Hwang, D., Kim, S., Monet, N., Koike, H., Bae, S.,
Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning,
WACV20(468-477)
IEEE DOI 2006
Pose estimation, Solid modeling, Heating systems, Training, Real-time systems BibRef

Das, S., Kishore, P.S.R., Bhattacharya, U.,
An End-To-End Framework For Pose Estimation of Occluded Pedestrians,
ICIP20(1446-1450)
IEEE DOI 2011
Pose estimation, Feature extraction, Training, Image segmentation, Detectors, Benchmark testing, Pose Estimation, Adversarial Learning BibRef

Zhang, K.[Kun], He, P.[Peng], Yao, P.[Ping], Chen, G.[Ge], Wu, R.[Rui], Du, M.[Min], Li, H.M.[Hui-Min], Fu, L.[Li], Zheng, T.Y.[Tian-Yao],
Learning Enhanced Resolution-Wise Features For Human Pose Estimation,
ICIP20(2256-2260)
IEEE DOI 2011
Pose estimation, Heating systems, Ground penetrating radar, Feature extraction, Random access memory, Training, Task analysis, Attention Mechanism BibRef

Scott, J.[Jesse], Ravichandran, B.[Bharadwaj], Funk, C.[Christopher], Collins, R.T.[Robert T.], Liu, Y.X.[Yan-Xi],
From Image to Stability: Learning Dynamics from Human Pose,
ECCV20(XXIII:536-554).
Springer DOI 2011
BibRef

Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.,
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation,
CVPR20(5385-5394)
IEEE DOI 2008
Heating systems, Pose estimation, Convolution, Spatial resolution, Deconvolution, Training BibRef

Ryou, S.[Serim], Jeong, S.G.[Seong-Gyun], Perona, P.[Pietro],
Anchor Loss: Modulating Loss Scale Based on Prediction Difficulty,
ICCV19(5991-6000)
IEEE DOI 2004
entropy, image classification, Modulation, learning (artificial intelligence), neural nets, pose estimation. BibRef

Duan, H., Lin, K., Jin, S., Liu, W., Qian, C., Ouyang, W.,
TRB: A Novel Triplet Representation for Understanding 2D Human Body,
ICCV19(9478-9487)
IEEE DOI 2004
image capture, image representation, learning (artificial intelligence), message passing, Convolution BibRef

Sharma, S., Varigonda, P.T., Bindal, P., Sharma, A., Jain, A.,
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking,
ICCV19(2325-2334)
IEEE DOI 2004
Code, Human Pose.
WWW Link. learning (artificial intelligence), neural nets, pose estimation, solid modelling, stereo image processing, Heating systems BibRef

Kocabas, M.[Muhammed], Karagoz, S.[Salih], Akbas, E.[Emre],
Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry,
CVPR19(1077-1086).
IEEE DOI 2002
BibRef

Martin, J.B.[Jesus Bujalance], Moutarde, F.[Fabien],
Real-time Gestural Control of Robot Manipulator Through Deep Learning Human-pose Inference,
CVS19(565-572).
Springer DOI 1912
BibRef

Tang, S., Tan, F., Cheng, K., Li, Z., Zhu, S., Tan, P.,
A Neural Network for Detailed Human Depth Estimation From a Single Image,
ICCV19(7749-7758)
IEEE DOI 2004
cameras, feature extraction, image colour analysis, learning (artificial intelligence), Skeleton BibRef

Cai, Y.J.[Yu-Jun], Ge, L.H.[Liu-Hao], Liu, J.[Jun], Cai, J.F.[Jian-Fei], Cham, T.J.[Tat-Jen], Yuan, J.S.[Jun-Song], Magnenat-Thalmann, N.[Nadia],
Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks,
ICCV19(2272-2281)
IEEE DOI 2004
convolutional neural nets, feature extraction, graph theory, image representation, image sequences, Kernel BibRef

Wang, B., Adeli, E., Chiu, H., Huang, D., Niebles, J.C.[Juan Carlos],
Imitation Learning for Human Pose Prediction,
ICCV19(7123-7132)
IEEE DOI 2004
image motion analysis, neural net architecture, pose estimation, recurrent neural nets, supervised learning, BibRef

Martinez, G.H., Raaj, Y., Idrees, H., Xiang, D., Joo, H., Simon, T., Sheikh, Y.,
Single-Network Whole-Body Pose Estimation,
ICCV19(6981-6990)
IEEE DOI 2004
Code, Human Pose.
WWW Link. computational complexity, face recognition, image resolution, learning (artificial intelligence), pose estimation BibRef

Iskakov, K., Burkov, E., Lempitsky, V., Malkov, Y.,
Learnable Triangulation of Human Pose,
ICCV19(7717-7726)
IEEE DOI 2004
Gaussian processes, object tracking, pose estimation, user interfaces, human pose, multiview 3D, triangulation methods, Detectors BibRef

Chen, C.H.[Ching-Hang], Tyagi, A.[Ambrish], Agrawal, A.[Amit], Drover, D.[Dylan], Rohith, M.V., Stojanov, S.[Stefan], Rehg, J.M.[James M.],
Unsupervised 3D Pose Estimation With Geometric Self-Supervision,
CVPR19(5707-5717).
IEEE DOI 2002
3D human pose from 2D skeletal joints. BibRef

Gong, K.[Ke], Gao, Y.M.[Yi-Ming], Liang, X.D.[Xiao-Dan], Shen, X.H.[Xiao-Hui], Wang, M.[Meng], Lin, L.[Liang],
Graphonomy: Universal Human Parsing via Graph Transfer Learning,
CVPR19(7442-7451).
IEEE DOI 2002
BibRef

Yang, L.[Lu], Song, Q.[Qing], Wang, Z.H.[Zhi-Hui], Jiang, M.[Ming],
Parsing R-CNN for Instance-Level Human Analysis,
CVPR19(364-373).
IEEE DOI 2002
BibRef

Zhao, L.[Long], Peng, X.[Xi], Tian, Y.[Yu], Kapadia, M.[Mubbasir], Metaxas, D.N.[Dimitris N.],
Semantic Graph Convolutional Networks for 3D Human Pose Regression,
CVPR19(3420-3430).
IEEE DOI 2002
BibRef

Chu, S.W., Song, Y., Zouo, J.J., Cai, W.,
Human Pose Estimation Using Deep Convolutional Densenet Hourglass Network with Intermediate Points Voting,
ICIP19(594-598)
IEEE DOI 1910
Deep learning, convolution neural network, human pose estimation. BibRef

Peng, X., Tang, Z., Yang, F., Feris, R.S., Metaxas, D.,
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation,
CVPR18(2226-2234)
IEEE DOI 1812
Training, Pose estimation, Generators, Neural networks, Task analysis, Data models BibRef

Rakhimov, R.[Ruslan], Bogomolov, E.[Emil], Notchenko, A.[Alexandr], Mao, F.[Fung], Artemov, A.[Alexey], Zorin, D.[Denis], Burnaev, E.[Evgeny],
Making DensePose fast and light,
WACV21(1868-1876)
IEEE DOI 2106
Performance evaluation, Computational modeling, Pipelines, Neural networks, Graphics processing units, Estimation BibRef

Gochoo, M., Tan, T.H., Alnajjar, F., Hsieh, J.W., Chen, P.Y.,
Lownet: Privacy Preserved Ultra-Low Resolution Posture Image Classification,
ICIP20(663-667)
IEEE DOI 2011
Vegetation, Indexes, Privacy, Ultra-low resolution, CNN, thermal image, posture classification, privacy preserving BibRef

Spurlock, S., Souvenir, R.,
Multimodal 3D Human Pose Estimation from a Single Image,
3DV19(663-670)
IEEE DOI 1806
Training, Mutual information, Pose estimation, Solid modeling, CNN BibRef

Rafi, U.[Umer], Leibe, B.[Bastian], Gall, J.[Juergen], Kostrikov, I.[Ilya],
An Efficient Convolutional Network for Human Pose Estimation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Tanabe, S.[Satoshi], Yamanaka, R.[Ryosuke], Tomono, M.[Mitsuru], Ito, M.[Makiko], Ishihara, T.[Teruo],
Real-Time Human Pose Estimation via Cascaded Neural Networks Embedded with Multi-task Learning,
CAIP17(II: 241-252).
Springer DOI 1708
BibRef

Marras, I.[Ioannis], Palasek, P.[Petar], Patras, I.[Ioannis],
Deep Mixture of MRFs for Human Pose Estimation,
ACCV18(III:717-733).
Springer DOI 1906
BibRef
And:
Deep Globally Constrained MRFs for Human Pose Estimation,
ICCV17(3486-3495)
IEEE DOI 1802
BibRef
Earlier:
Deep Refinement Convolutional Networks for Human Pose Estimation,
FG17(446-453)
IEEE DOI 1707
Markov processes, convolution, neural net architecture, pose estimation, random processes, ConvNet, Markov Random Field. Adaptation models, Data models, Heating systems, Image resolution, Pose estimation BibRef

Nie, X., Feng, J., Zuo, Y., Yan, S.,
Human Pose Estimation with Parsing Induced Learner,
CVPR18(2100-2108)
IEEE DOI 1812
Pose estimation, Adaptation models, Feature extraction, Predictive models, Semantics, Task analysis, Benchmark testing BibRef

Yang, W., Ouyang, W., Wang, X., Ren, J., Li, H., Wang, X.,
3D Human Pose Estimation in the Wild by Adversarial Learning,
CVPR18(5255-5264)
IEEE DOI 1812
Pose estimation, Generators, Heating systems, Joints, Task analysis BibRef

Nie, X.C.[Xue-Cheng], Feng, J.[Jiashi], Yan, S.C.[Shui-Cheng],
Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation,
ECCV18(VI: 519-534).
Springer DOI 1810
BibRef

Tang, W.[Wei], Yu, P.[Pei], Wu, Y.[Ying],
Deeply Learned Compositional Models for Human Pose Estimation,
ECCV18(III: 197-214).
Springer DOI 1810
BibRef

Tekin, B., Márquez-Neila, P., Salzmann, M., Fua, P.,
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation,
ICCV17(3961-3970)
IEEE DOI 1802
image fusion, learning (artificial intelligence), motion estimation, pose estimation, 2D joint locations, BibRef

Ronchi, M.R.[Matteo Ruggero], Perona, P.[Pietro],
Benchmarking and Error Diagnosis in Multi-instance Pose Estimation,
ICCV17(369-378)
IEEE DOI 1802
computer vision, estimation theory, learning (artificial intelligence), pose estimation, Wrist BibRef

Wang, H., Liang, W., Yu, L.F.,
Transferring Objects: Joint Inference of Container and Human Pose,
ICCV17(2952-2960)
IEEE DOI 1802
computer vision, inference mechanisms, learning (artificial intelligence), pose estimation, BibRef

Liu, B., Ferrari, V.,
Active Learning for Human Pose Estimation,
ICCV17(4373-4382)
IEEE DOI 1802
learning (artificial intelligence), pose estimation, active learning framework, active learning process, Uncertainty BibRef

Sun, K., Lan, C., Xing, J., Zeng, W., Liu, D., Wang, J.,
Human Pose Estimation Using Global and Local Normalization,
ICCV17(5600-5608)
IEEE DOI 1802
convolution, learning (artificial intelligence), neural nets, pose estimation, articulated pose estimation, Wrist BibRef

Yang, W.[Wei], Li, S.[Shuang], Ouyang, W.L.[Wan-Li], Li, H.S.[Hong-Sheng], Wang, X.G.[Xiao-Gang],
Learning Feature Pyramids for Human Pose Estimation,
ICCV17(1290-1299)
IEEE DOI 1802
computer vision, convolution, feature extraction, image classification, image representation, Visualization BibRef

Ai, B., Zhou, Y.[Yu], Yu, Y.[Yao], Du, S.[Sidan],
Human Pose Estimation Using Deep Structure Guided Learning,
WACV17(1224-1231)
IEEE DOI 1609
Biological system modeling, Feature extraction, Heating systems, Kernel, Neural networks, Pose, estimation
See also Markerless Motion Capture of Human Body Using PSO with Single Depth Camera. BibRef

Azrour, S.[Samir], Piérard, S.[Sébastien], Geurts, P.[Pierre], van Droogenbroeck, M.[Marc],
A Two-Step Methodology for Human Pose Estimation Increasing the Accuracy and Reducing the Amount of Learning Samples Dramatically,
ACIVS17(3-14).
Springer DOI 1712
BibRef

Ghezelghieh, M.F., Kasturi, R., Sarkar, S.,
Learning Camera Viewpoint Using CNN to Improve 3D Body Pose Estimation,
3DV16(685-693)
IEEE DOI 1701
cameras BibRef

Brau, E., Jiang, H.,
3D Human Pose Estimation via Deep Learning from 2D Annotations,
3DV16(582-591)
IEEE DOI 1701
cameras BibRef

Kwak, S.[Suha], Cho, M.S.[Min-Su], Laptev, I.[Ivan],
Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation,
CVPR16(4938-4947)
IEEE DOI 1612
BibRef

Zhou, X.Y.[Xing-Yi], Sun, X.[Xiao], Zhang, W.[Wei], Liang, S.[Shuang], Wei, Y.C.[Yi-Chen],
Deep Kinematic Pose Regression,
DeepLearn16(III: 186-201).
Springer DOI 1611
BibRef

Wang, L.[Lei], Zhao, X.[Xu], Liu, Y.C.[Yun-Cai],
Adaptive appearance learning for human pose estimation,
ICIP15(1125-1129)
IEEE DOI 1512
BibRef

Singh, S.[Saurabh], Hoiem, D.[Derek], Forsyth, D.A.[David A.],
Learning a sequential search for landmarks,
CVPR15(3422-3430)
IEEE DOI 1510
humans or birds. BibRef

Tompson, J.[Jonathan], Goroshin, R.[Ross], Jain, A.[Arjun], Le Cun, Y.L.[Yann L.], Bregler, C.[Christoph],
Efficient object localization using Convolutional Networks,
CVPR15(648-656)
IEEE DOI 1510
BibRef

Fan, X.C.[Xiao-Chuan], Zheng, K.[Kang], Lin, Y.W.[Yue-Wei], Wang, S.[Song],
Combining local appearance and holistic view: Dual-Source Deep Neural Networks for human pose estimation,
CVPR15(1347-1355)
IEEE DOI 1510
BibRef

Lehrmann, A.M.[Andreas M.], Gehler, P.V.[Peter V.], Nowozin, S.[Sebastian],
A Non-parametric Bayesian Network Prior of Human Pose,
ICCV13(1281-1288)
IEEE DOI 1403
Bayesian network BibRef

Joo, J.[Jungseock], Wang, S.[Shuo], Zhu, S.C.[Song-Chun],
Human Attribute Recognition by Rich Appearance Dictionary,
ICCV13(721-728)
IEEE DOI 1403
Fine-grained Recognition; Human Attribute; Weakly-Supervised Learning BibRef

Gong, W.J.[Wen-Juan], Brauer, J.[Jurgen], Arens, M.[Michael], Gonzalez, J.[Jordi],
Modeling vs. learning approaches for monocular 3D human pose estimation,
PEAction11(1287-1294).
IEEE DOI 1201
BibRef
And: A2, A1, A4, A3:
On the effect of temporal information on monocular 3d human pose estimation,
ARTEMIS11(906-913).
IEEE DOI 1201

See also Voting Strategies for Anatomical Landmark Localization Using the Implicit Shape Model. BibRef

Wang, Y.[Yang], Tran, D.[Duan], Liao, Z.C.[Zi-Cheng],
Learning hierarchical poselets for human parsing,
CVPR11(1705-1712).
IEEE DOI 1106
BibRef

Hur, D.C.[Dong-Cheol], Wallraven, C.[Christian], Lee, S.W.[Seong-Whan],
View Invariant Body Pose Estimation Based on Biased Manifold Learning,
ICPR10(3866-3869).
IEEE DOI 1008
BibRef

Guo, W.W.[Wei-Wei], Patras, I.[Ioannis],
Learning Output-kernel-dependent Regression for Human Pose Estimation,
BMVCWS10(xx-yy).
HTML Version. 1009
BibRef
Earlier:
Discriminative 3D human pose estimation from monocular images via topological preserving hierarchical affinity clustering,
S3DV09(9-15).
IEEE DOI 0910
BibRef

Zhu, L.L.[Long Leo], Chen, Y.H.[Yuan-Hao], Lu, Y.F.[Yi-Fei], Lin, C.X.[Chen-Xi], Yuille, A.L.[Alan L.],
Max Margin AND/OR Graph learning for parsing the human body,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Navaratnam, R.[Ramanan], Fitzgibbon, A.W.[Andrew W.],
Semi-supervised Learning of Joint Density Models for Human Pose Estimation,
BMVC06(II:79).
PDF File. 0609
BibRef

Hua, G.[Gang], Yang, M.H.[Ming-Hsuan], Wu, Y.[Ying],
Learning to Estimate Human Pose with Data Driven Belief Propagation,
CVPR05(II: 747-754).
IEEE DOI 0507
BibRef

Nakajima, C.,
Posture recognition of nuclear power plant operators by supervised learning,
ICIP04(II: 877-880).
IEEE DOI 0505
BibRef

Shakhnarovich, G.[Gregory], Viola, P.A.[Paul A.], Darrell, T.J.[Trevor J.],
Fast Pose Estimation with Parameter-Sensitive Hashing,
ICCV03(750-757).
IEEE DOI 0311
BibRef
And: MIT AIMAIM-2003-009, April 18, 2003.
WWW Link. learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. 0306
BibRef

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Multi-Person Human Pose Desicriptions .


Last update:Nov 30, 2021 at 22:19:38