Related Concepts
Definition
Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular (While the problem of human pose estimation can be formulated from simultaneous observations from multiple camera views (or one or more RGBD cameras), which can result in higher-fidelity results or alleviate annotation [46], such formulations are substantially less common, as they require cumbersome hardware setups, making them inappropriate for many applications.), image or video. The pose can be expressed in variety of ways (e.g., joint positions/keypoints or angles between body parts) in either the image (2d) or the world (3d) coordinate frame.
Background
Human pose estimation is one of the key fundamental problems in computer vision that has been studied for well over 20 years. The reason for its importance is the abundance of...
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References
Agarwal A, Triggs B (2006) Recovering 3D human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1): 44–58
Alp Guler R, Trigeorgis G, Antonakos E, Snape P, Zafeiriou S, Kokkinos I (2017) Densereg: fully convolutional dense shape regression in-the-wild. In: IEEE Conference on Computer Vision and Pattern Recognition
Alp Guler R, Neverova N, Kokkinos I (2018) Densepose: dense human pose estimation in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition
Andriluka M, Roth S, Schiele B (2009) Pictorial structures revisited: people detection and articulated pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition
Andriluka M, Roth S, Schiele B (2010) Monocular 3D pose estimation and tracking by detection. In: IEEE Conference on Computer Vision and Pattern Recognition
Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2D human pose estimation: new benchmark and state of the art analysis. In: IEEE Conference on Computer Vision and Pattern Recognition
Bergtholdt M, Kappes J, Schmidt S, Schnorr C (2010) A study of parts-based object class detection using complete graphs. International Journal of Computer Vision 87: 93–117
Bo L, Sminchisescu C (2010) Twin gaussian processes for structured prediction. International Journal of Computer Vision 87:28–52
Bo L, Sminchisescu C, Kanaujia A, Metaxas D (2008) Fast algorithms for large scale conditional 3D prediction. In: IEEE Conference on Computer Vision and Pattern Recognition
Cai Y, Ge L, Liu J, Cai J, Cham T-J, Yuan J, Magnenat Thalmann N (2019) Exploiting spatial-temporal relationships for 3D pose estimation via graph convolutional networks. In: IEEE International Conference on Computer Vision
Cao Z, Hidalgo Martinez G, Simon T, Wei S, Sheikh YA (2019) Openpose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1):1–1
Carreira J, Fragkiadaki K, Agrawal P, Malik J (2016) Human pose estimation with iterative error feedback. In: IEEE Conference on Computer Vision and Pattern Recognition
Chen C-H, Tyagi A, Agrawal A, Drover D, MV R, Stojanov S, Rehg JM Unsupervised 3D pose estimation with geometric self-supervision. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
de Bem R, Arnab A, Golodetz S, Sapienza M, Torr P (2018) Deep fully-connected part-based models for human pose estimation. Machine Learning Research 95:327–342
Eichner M, Ferrari V (2010) We are family: joint pose estimation of multiple persons. In: European Conference on Computer Vision
Fang H-S, Xie S, Tai Y-W, Lu C (2017) RMPE: regional multi-person pose estimation. In: IEEE International Conference on Computer Vision
Felzenszwalb PF, Huttenlocher DP (2005) Pictorial structures for object recognition. International Journal of Computer Vision 61(1):55–79
Ferrari V, Marn-Jimnez MJ, Zisserman A (2008) Progressive search space reduction for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition
Gall J, Rosenhahn B, Brox T, Seidel H-P (2010) Optimization and filtering for human motion capture. International Journal of Computer Vision 87(1–2):75–92
Girshick R, Iandola F, Darrell T, Malik J (2015) Deformable part models are convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask RCNN. In: IEEE International Conference on Computer Vision
Ionescu C, Papava D, Olaru V, Sminchisescu C (2014) Human3.6m: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Transactions on Pattern Analysis and Machine Intelligence 36:1325–1339
Jiang H (2009) Human pose estimation using consistent max-covering. In: IEEE International Conference on Computer Vision
Kanaujia A, Sminchisescu C, Metaxas D (2007) Semi-supervised hierarchical models for 3D human pose reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition
Kanazawa A, Black MJ, Jacobs DW, Malik J (2018) End-to-end recovery of human shape and pose. In: IEEE Conference on Computer Vision and Pattern Recognition
Kiciroglu S, Rhodin H, Sinha S, Salzmann M, Fua P (2020) Activemocap: optimized drone flight for active human motion capture. In: IEEE Conference on Computer Vision and Pattern Recognition
Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge
Kolotouros N, Pavlakos G, Black MJ, Daniilidis K (2019) Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: IEEE International Conference on Computer Vision
Lee MW, Cohen I (2004) Proposal maps driven MCMC for estimating human body pose in static images. In: IEEE Conference on Computer Vision and Pattern Recognition
Lin T-Y, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Dollár P (2014) Microsoft coco: common objects in context. In: European Conference on Computer Vision
Loper M, Mahmood N, Romero J, Pons-Moll G, Black MJ (2015) SMPL: a skinned multi-person linear model. ACM SIGGRAPH Asia 34(6):1–16
Martinez J, Hossain R, Romero J, Little JJ (2017) A simple yet effective baseline for 3D human pose estimation. In: IEEE International Conference on Computer Vision, pp 2640–2649
Mehta D, Sotnychenko O, Mueller F, Xu W, Elgharib M, Fua P, Seidel H-P, Rhodin H, Pons-Moll G, Theobalt C (2020) XNect: real-time multi-person 3D human pose estimation with a single RGB camera. In: ACM SIGGRAPH
Mori G, Ren X, Efros A, Malik J (2004) Recovering human body configurations: combining segmentation and recognition. In: IEEE Conference on Computer Vision and Pattern Recognition
Navaratnam R, Fitzgibbon A, Cipolla R (2007) The joint manifold model for semi-supervised multi-valued regression. In: IEEE International Conference on Computer Vision
Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision
Papandreou G, Kanazawa N, Zhu T, Toshev A, Tompson J, Bregler C, Murphy K (2017) Towards accurate multi-person pose estimation in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition
Papandreou G, Zhu T, Chen L-C, Gidaris S, Tompson J, Murphy K (2018) Personlab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In: European Conference on Computer Vision
Pavlakos G, Zhou X, Derpanis K, Daniilidis K (2017) Coarse-to-fine volumetric prediction for single-image 3D human pose. In: IEEE Conference on Computer Vision and Pattern Recognition
Peng XB, Kanazawa A, Malik J, Abbeel P, Levine S (2018) SFV: reinforcement learning of physical skills from videos. ACM Trans Graph 37:1–14
Pishchulin L, Insafutdinov E, Tang S, Andres B, Andriluka M, Gehler P, Schiele B (2016) Deepcut: joint subset partition and labeling for multi-person pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition
Ramanan D (2006) Learning to parse images of articulated bodies. Neural Information and Processing Systems 19:1129–1136
Ren X, Berg AC, Malik J (2005) Recovering human body configurations using pair-wise constraints between parts. In: International Conference on Computer Vision
Rhodin H, Salzmann M, Fua P (2018) Unsupervised geometry-aware representation for 3D human pose estimation. In: European Conference on Computer Vision
Rhodin H, Sporri J, Katircioglu I, Constantin V, Meyer F, Muller E, Salzmann M, Fua P (2018) Learning monocular 3D human pose estimation from multi-view images. In: IEEE Conference on Computer Vision and Pattern Recognition
Rhodin H, Constantin V, Katircioglu I, Salzmann M, Fua P (2019) Neural scene decomposition for multi-person motion capture. In: IEEE Conference on Computer Vision and Pattern Recognition
Shakhnarovich G, Viola P, Darrell T (2003) Fast pose estimation with parameter sensitive hashing. In: International Conference on Computer Vision
Sigal L, Black MJ (2006) Measure locally, reason globally: occlusion-sensitive articulated pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition
Sigal L, Isard M, Sigelman BH, Black MJ (2003) Attractive people: assembling loose-limbed models using non-parametric belief propagation. Advances in Neural Information Processing Systems 16:1539–1546
Sigal L, Balan A, Black MJ (2007) Combined discriminative and generative articulated pose and non-rigid shape estimation. In: Neural Information and Processing Systems
Sigal L, Memisevic R, Fleet DJ (2009) Shared kernel information embedding for discriminative inference. In: IEEE Conference on Computer Vision and Pattern Recognition
Singh VK, Nevatia R, Huang C (2010) Efficient inference with multiple heterogeneous part detectors for human pose estimation. In: European Conference on Computer Vision, pp 314–327
Sun K, Xiao B, Liu D, Wang J (2019) Deep high-resolution representation learning for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition
Tian T-P, Sclaroff S (2010) Fast globally optimal 2D human detection with loopy graph models. In: IEEE Conference on Computer Vision and Pattern Recognition
Tompson JJ, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp 1799–1807
Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C (2015) Efficient object localization using convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition
Toshev A, Szegedy C (2014) Deeppose: human pose estimation via deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition
Urtasun R, Darrell T (2008) Sparse probabilistic regression for activity-independent human pose inference. In: IEEE Conference on Computer Vision and Pattern Recognition
Wei S-E, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines. In: IEEE Conference on Computer Vision and Pattern Recognition
Xiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. In: European Conference on Computer Vision
Xu Y, Zhu S-C, Tung T (2019) Denserac: joint 3D pose and shape estimation by dense render-and-compare. In: IEEE International Conference on Computer Vision
Yang Y, Ramanan D (2011) Articulated pose estimation with flexible mixture-of-parts. In: IEEE Conference on Computer Vision and Pattern Recognition
Zhang J, Luo J, Collins R, Liu Y (2006) Body localization in still images using hierarchical models and hybrid search. In: IEEE Conference on Computer Vision and Pattern Recognition
Zhang H, Ouyang H, Liu S, Qi X, Shen X, Yang R, Jia J (2019) Human pose estimation with spatial contextual information. arXiv preprint arXiv:1901.01760
Zhang JY, Felsen P, Kanazawa A, Malik J (2019) Predicting 3D human dynamics from video. In: International Conference on Computer Vision
Zhao L, Peng X, Tian Y, Kapadia M, Metaxas DN (2019) Semantic graph convolutional networks for 3D human pose regression. In: IEEE Conference on Computer Vision and Pattern Recognition
Zuffi S, Black MJ (2015) The stitched puppet: a graphical model of 3d human shape and pose. In: IEEE Conference on Computer Vision and Pattern Recognition
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Sigal, L. (2021). Human Pose Estimation. In: Ikeuchi, K. (eds) Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-63416-2_584
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DOI: https://doi.org/10.1007/978-3-030-63416-2_584
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