21.4 Gesture and Pose Analysis

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

Park, S.[Seyoung], Nie, B.X.[Bruce Xiaohan], Zhu, S.C.[Song-Chun],
Attribute And-Or Grammar for Joint Parsing of Human Pose, Parts and Attributes,
PAMI(40), No. 7, July 2018, pp. 1555-1569.
IEEE DOI 1806
Geometry, Glass, Grammar, Head, Magnetic heads, Pose estimation, Predictive models, And-Or grammar, Attribute grammar, pose estimation BibRef

Nie, B.X., Wei, P., Zhu, S.C.,
Monocular 3D Human Pose Estimation by Predicting Depth on Joints,
ICCV17(3467-3475)
IEEE DOI 1802
image motion analysis, pose estimation, recurrent neural nets, 2D human joint locations, HHOI dataset, Human3.6M dataset, BibRef

Petrov, I.[Ilia], Shakhuro, V.[Vlad], Konushin, A.[Anton],
Deep probabilistic human pose estimation,
IET-CV(12), No. 5, August 2018, pp. 578-585.
DOI Link 1807
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., 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

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

Dong, L., Chen, X., Wang, R., Zhang, Q., Izquierdo, E.[Ebroul],
ADORE: An Adaptive Holons Representation Framework for Human Pose Estimation,
CirSysVideo(28), No. 10, October 2018, pp. 2803-2813.
IEEE DOI 1811
Pose estimation, Detectors, Adaptation models, Training, Computational modeling, Image color analysis, Pose estimation, adaptivity 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

Iqbal, U.[Umar], Doering, A.[Andreas], Yasin, H.[Hashim], Krüger, B.[Björn], Weber, A.[Andreas], Gall, J.[Juergen],
A dual-source approach for 3D human pose estimation from single images,
CVIU(172), 2018, pp. 37-49.
Elsevier DOI 1812
BibRef
Earlier: A2, A1, A4, A5, A6, Only:
A Dual-Source Approach for 3D Pose Estimation from a Single Image,
CVPR16(4948-4956)
IEEE DOI 1612
3D human pose estimation, Motion capture, 3D reconstruction, Articulated pose estimation BibRef

Liang, S.[Shuang], Sun, X.[Xiao], Wei, Y.C.[Yi-Chen],
Compositional Human Pose Regression,
CVIU(176-177), 2018, pp. 1-8.
Elsevier DOI 1812
BibRef

Sun, X.[Xiao], Xiao, B.[Bin], Wei, F.Y.[Fang-Yin], Liang, S.[Shuang], Wei, Y.C.[Yi-Chen],
Integral Human Pose Regression,
ECCV18(VI: 536-553).
Springer DOI 1810
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

Yang, J.J.[Jing-Jing], Wan, L.[Lili], Xu, W.[Wanru], Wang, S.H.[Sheng-Hui],
3D human pose estimation from a single image via exemplar augmentation,
JVCIR(59), 2019, pp. 371-379.
Elsevier DOI 1903
Human pose estimation, Human pose recovery, Exemplar-based, Pose retrieval, Pose synthesis, Monocular BibRef

Jiang, Y., Chi, Z.,
A CNN Model for Semantic Person Part Segmentation With Capacity Optimization,
IP(28), No. 5, May 2019, pp. 2465-2478.
IEEE DOI 1903
computational complexity, convolutional neural nets, image segmentation, learning (artificial intelligence), capacity optimization BibRef

Gilbert, A.[Andrew], Trumble, M.[Matthew], Hilton, A.[Adrian], Collomosse, J.[John],
Fusing Visual and Inertial Sensors with Semantics for 3D Human Pose Estimation,
IJCV(127), No. 4, April 2019, pp. 381-397.
Springer DOI 1903
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

Liu, S., Li, Y., Hua, G.,
Human Pose Estimation in Video via Structured Space Learning and Halfway Temporal Evaluation,
CirSysVideo(29), No. 7, July 2019, pp. 2029-2038.
IEEE DOI 1907
Pose estimation, Streaming media, Maintenance engineering, Convolution, Optimization methods, Human pose estimation, structure learning BibRef

Zhang, X.Y.[Xiao-Yan], Tang, Z.H.[Zhen-Hua], Hou, J.[Junhui], Hao, Y.B.[Yan-Bin],
3D human pose estimation via human structure-aware fully connected network,
PRL(125), 2019, pp. 404-410.
Elsevier DOI 1909
3D human pose estimation, Human structure, Fully connected network 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

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

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

Pavllo, D.[Dario], Feichtenhofer, C.[Christoph], Auli, M.[Michael], Grangier, D.[David],
Modeling Human Motion with Quaternion-Based Neural Networks,
IJCV(128), No. 4, April 2020, pp. 855-872.
Springer DOI 2004
BibRef
Earlier: A1, A2, A4, A3:
3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training,
CVPR19(7745-7754).
IEEE DOI 2002
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

Wen, L.Y.[Ling-Yun], Guo, G.D.[Guo-Dong],
A computational approach to body mass index prediction from face images,
IVC(31), No. 5, May 2013, pp. 392-400.
Elsevier DOI 1305
Body mass index (BMI); Facial features; BMI prediction; Machine vision; Large database BibRef

Jiang, M.[Min], Shang, Y.Y.[Yuan-Yuan], Guo, G.D.[Guo-Dong],
Computational approach to body mass index estimation from dressed people in 3D space,
IET-IPR(14), No. 7, 29 May 2020, pp. 1248-1256.
DOI Link 2005
BibRef

Miki, D.[Daisuke], Abe, S.[Shinya], Chen, S.[Shi], Demachi, K.[Kazuyuki],
Robust human pose estimation from distorted wide-angle images through iterative search of transformation parameters,
SIViP(14), No. 4, June 2020, pp. 693-700.
WWW Link. 2005
BibRef

Zhao, C.C.[Chen-Chen], Qian, Y.Q.[Ye-Qiang], Yang, M.[Ming],
Monocular pedestrian orientation estimation based on deep 2D-3D feedforward,
PR(100), 2020, pp. 107182.
Elsevier DOI 2005
Information feedforward, Logic relationship, Monocular vision, Orientation estimation BibRef

Nie, X.C.[Xue-Cheng], Li, Y.C.[Yun-Cheng], Luo, L.J.[Lin-Jie], Zhang, N.[Ning], Feng, J.S.[Jia-Shi],
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos,
ICCV19(6941-6949)
IEEE DOI 2004
learning (artificial intelligence), neural nets, pose estimation, video signal processing, light-weight distillator, temporal cues, Knowledge engineering 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

Rong, Y., Liu, Z., Li, C., Cao, K., Loy, C.C.,
Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild,
ICCV19(5339-5347)
IEEE DOI 2004
feature extraction, image annotation, image reconstruction, image segmentation, learning (artificial intelligence), 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], Thalmann, N.M.[Nadia Magnenat],
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

Li, Z., Wang, X., Wang, F., Jiang, P.,
On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos,
ICCV19(2192-2201)
IEEE DOI 2004
learning (artificial intelligence), pose estimation, richly annotated training data, training dataset, baseline model, Feature extraction BibRef

Wang, B., Adeli, E., Chiu, H., Huang, D., Niebles, J.C.,
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

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

Cahill-Lane, J., Mills, S., Duncan, S.,
Body Part Labelling with Minkowski Networks,
IVCNZ19(1-6)
IEEE DOI 2004
computer vision, feature extraction, image classification, image representation, learning (artificial intelligence), Minkowski networks BibRef

Bieler, D., Günel, S.G., Fua, P., Rhodin, H.,
Gravity as a Reference for Estimating a Person's Height From Video,
ICCV19(8568-8576)
IEEE DOI 2004
calibration, cameras, estimation theory, geometry, image motion analysis, image sequences, object detection, Estimation BibRef

Venkat, A., Patel, C., Agrawal, Y., Sharma, A.,
HumanMeshNet: Polygonal Mesh Recovery of Humans,
3D-Wild19(2178-2187)
IEEE DOI 2004
computational geometry, computer vision, image reconstruction, image representation, image resolution, SMPL BibRef

Hassan, M., Choutas, V., Tzionas, D., Black, M.,
Resolving 3D Human Pose Ambiguities With 3D Scene Constraints,
ICCV19(2282-2292)
IEEE DOI 2004
Code, Human Pose.
WWW Link. image motion analysis, image reconstruction, image sequences, pose estimation, 3D human pose ambiguities, scene constraints, 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

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, Three-dimensional displays 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

Zhang, S.H.[Song-Hai], Li, R.L.[Rui-Long], Dong, X.[Xin], Rosin, P.[Paul], Cai, Z.[Zixi], Han, X.[Xi], Yang, D.C.[Ding-Cheng], Huang, H.Z.[Hao-Zhi], Hu, S.M.[Shi-Min],
Pose2Seg: Detection Free Human Instance Segmentation,
CVPR19(889-898).
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

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

Li, C.[Chen], Lee, G.H.[Gim Hee],
Generating Multiple Hypotheses for 3D Human Pose Estimation With Mixture Density Network,
CVPR19(9879-9887).
IEEE DOI 2002
BibRef

Chen, X.P.[Xi-Peng], Lin, K.Y.[Kwan-Yee], Liu, W.T.[Wen-Tao], Qian, C.[Chen], Lin, L.[Liang],
Weakly-Supervised Discovery of Geometry-Aware Representation for 3D Human Pose Estimation,
CVPR19(10887-10896).
IEEE DOI 2002
BibRef

Habibie, I.[Ikhsanul], Xu, W.[Weipeng], Mehta, D.[Dushyant], Pons-Moll, G.[Gerard], Theobalt, C.[Christian],
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations,
CVPR19(10897-10906).
IEEE DOI 2002
BibRef

Kreiss, S.[Sven], Bertoni, L.[Lorenzo], Alahi, A.[Alexandre],
PifPaf: Composite Fields for Human Pose Estimation,
CVPR19(11969-11978).
IEEE DOI 2002
BibRef

Arnab, A.[Anurag], Doersch, C.[Carl], Zisserman, A.[Andrew],
Exploiting Temporal Context for 3D Human Pose Estimation in the Wild,
CVPR19(3390-3399).
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

Zhang, F.[Feng], Zhu, X.T.[Xia-Tian], Ye, M.[Mao],
Fast Human Pose Estimation,
CVPR19(3512-3521).
IEEE DOI 2002
BibRef

Li, Y.[Yining], Huang, C.[Chen], Loy, C.C.[Chen Change],
Dense Intrinsic Appearance Flow for Human Pose Transfer,
CVPR19(3688-3697).
IEEE DOI 2002
BibRef

Moon, G.[Gyeongsik], Chang, J.Y.[Ju Yong], Lee, K.M.[Kyoung Mu],
PoseFix: Model-Agnostic General Human Pose Refinement Network,
CVPR19(7765-7773).
IEEE DOI 2002
BibRef

Wandt, B.[Bastian], Rosenhahn, B.[Bodo],
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation,
CVPR19(7774-7783).
IEEE DOI 2002
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

Knyaz, V.A., Maksimov, A.A., Novikov, M.M.,
Vision Based Automated Anthropological Measurements and Analysis,
PTVSBB19(117-122).
DOI Link 1912
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

Benzine, A., Luvison, B., Pham, Q.C., Achard, C.,
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, estimation BibRef

Neumann, L.[Lukáš], Vedaldi, A.[Andrea],
Tiny People Pose,
ACCV18(III:558-574).
Springer DOI 1906
BibRef

Zhuang, W.L.[Wen-Lin], Peng, C.[Cong], Xia, S.[Siyu], Wang, Y.G.[Yan-Gang],
Multi-scale Adaptive Structure Network for Human Pose Estimation from Color Images,
ACCV18(I:643-658).
Springer DOI 1906
BibRef

Yoshiyasu, Y.[Yusuke], Sagawa, R.[Ryusuke], Ayusawa, K.[Ko], Murai, A.[Akihiko],
Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image,
ACCV18(IV:485-500).
Springer DOI 1906
BibRef

Pelka, O.[Obioma], Nensa, F.[Felix], Friedrich, C.M.[Christoph M.],
Optimizing Body Region Classification with Deep Convolutional Activation Features,
WiCV-E18(IV:699-704).
Springer DOI 1905
BibRef

Sayed, M.R., Sim, T., Lim, J., Ma, K.T.,
Which Body Is Mine?,
WACV19(829-838)
IEEE DOI 1904
anthropometry, convolutional neural nets, correlation methods, image matching, pose estimation, Image recognition BibRef

Nibali, A., He, Z., Morgan, S., Prendergast, L.,
3D Human Pose Estimation With 2D Marginal Heatmaps,
WACV19(1477-1485)
IEEE DOI 1904
image colour analysis, inference mechanisms, neural nets, pose estimation, statistical analysis, stereo image processing, Solid modeling BibRef

Polania, L., Fung, G., Wang, D.,
Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation,
WACV19(782-790)
IEEE DOI 1904
face recognition, image denoising, pattern classification, regression analysis, search problems, Siamese architectures, Search problems BibRef

Wang, D.[Dan], Sheng, Y.[Yun], Zhang, G.[Gui_Xu],
A New Female Body Segmentation and Feature Localisation Method for Image-Based Anthropometry,
MMMod19(I:567-577).
Springer DOI 1901
BibRef

Luo, Y.[Yue], Ren, J.[Jimmy], Wang, Z.X.[Zhou-Xia], Sun, W.X.[Wen-Xiu], Pan, J.S.[Jin-Shan], Pang, J.H.[Jia-Hao], Lin, L.[Liang],
LSTM Pose Machines,
CVPR18(5207-5215)
IEEE DOI 1812
Videos, Pose estimation, Computational modeling, Recurrent neural networks, Computer architecture, Benchmark testing 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

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

Fang, H., Lu, G., Fang, X., Xie, J., Tai, Y., Lu, C.,
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer,
CVPR18(70-78)
IEEE DOI 1812
Transforms, Computer vision, Pattern recognition, Knowledge transfer, Image segmentation BibRef

Saint, A., Ahmed, E., Shabayek, A.E.R., Cherenkova, K., Gusev, G., Aouada, D., Ottersten, B.,
3DBodyTex: Textured 3D Body Dataset,
3DV18(495-504)
IEEE DOI 1812
computer vision, image resolution, image texture, solid modelling, textured 3D body dataset, static 3D body scans, 3D body scans BibRef

Fieraru, M., Khoreva, A., Pishchulin, L., Schiele, B.,
Learning to Refine Human Pose Estimation,
Crowd18(318-31809)
IEEE DOI 1812
Pose estimation, Task analysis, Predictive models, Videos, Training, Data models, Benchmark testing BibRef

Andriluka, M., Iqbal, U., Insafutdinov, E., Pishchulin, L., Milan, A., Gall, J., Schiele, B.,
PoseTrack: A Benchmark for Human Pose Estimation and Tracking,
CVPR18(5167-5176)
IEEE DOI 1812
Pose estimation, Benchmark testing, Task analysis, Tracking, Training, Video sequences, Sports 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

Güler, R.A., Neverova, N., Kokkinos, I.,
DensePose: Dense Human Pose Estimation in the Wild,
CVPR18(7297-7306)
IEEE DOI 1812
Pose estimation, Task analysis, Solid modeling, Training, Pipelines, Deformable models BibRef

Pavlakos, G., Zhou, X., Daniilidis, K.,
Ordinal Depth Supervision for 3D Human Pose Estimation,
CVPR18(7307-7316)
IEEE DOI 1812
Training, Image reconstruction, Pose estimation, Hip, Benchmark testing 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

Chen, Q., Zhang, C., Liu, W., Wang, D.,
SHPD: Surveillance Human Pose Dataset and Performance Evaluation for Coarse-Grained Pose Estimation,
ICIP18(4088-4092)
IEEE DOI 1809
Surveillance, Pose estimation, Cameras, Lighting, Benchmark testing, Task analysis, Performance evaluation, pose estimation, global-pose BibRef

Hu, H.M.[Hui-Min], Bai, Y.H.[Ya-Hui], Li, Y.X.[Yin-Xia], Wu, H.M.[Hai-Mei], Luo, L.[Ling], Wang, R.[Rui], Hong, P.[Pu],
Research on Ergonomics Design of the Height and Operation Force for Furniture Lockset,
DHM18(64-74).
Springer DOI 1807
BibRef

Friemert, D.[Daniel], Saala, F.[Florian], Hartmann, U.[Ulrich], Ellegast, R.[Rolf],
Similarities and Differences in Posture During Simulated Order Picking in Real Life and Virtual Reality,
DHM18(41-53).
Springer DOI 1807
BibRef

McLaughlin, N.[Niall], Martinez del Rincon, J.[Jesus],
Refining the Pose: Training and Use of Deep Recurrent Autoencoders for Improving Human Pose Estimation,
AMDO18(11-23).
Springer DOI 1807
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

Yang, Z., Luo, J.,
Personalized pose estimation for body language understanding,
ICIP17(126-130)
IEEE DOI 1803
Heating systems, Pose estimation, Reliability, Task analysis, Videos, Visualization, Wrist, Body Parts Tracking, Pose Estimation BibRef

Tekin, B., Sinha, S.N., Fua, P.,
Real-Time Seamless Single Shot 6D Object Pose Prediction,
CVPR18(292-301)
IEEE DOI 1812
Pose estimation, Solid modeling, Task analysis, Computer architecture 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