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We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device.
J. T. Reason and J. J. Brand, Motion sickness. Academic press, 1975
1975
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics , 2010, pp. 249–256
2010
Earlier work this paper cites.
A. Fathi, A. Farhadi, and J. M. Rehg, “Understanding egocentric activities,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2011
2011
Earlier work this paper cites.
G. Pons-Moll, A. Baak, J. Gall, L. Leal-Taixe, M. Mueller, H.-P. Seidel, and B. Rosenhahn, “Outdoor human motion capture using inverse kinematics and von mises-fisher sampling,” in IEEE International Conference on Computer Vision (ICCV) , nov 2011, pp. 1243–1250
2011
Earlier work this paper cites.
T. Shiratori, H. S. Park, L. Sigal, Y. Sheikh, and J. K. Hodgins, “Motion capture from body-mounted cameras,” in ACM Transactions on Graphics (TOG) , vol. 30, no. 4. ACM, 2011, p. 31
2011
Earlier work this paper cites.
C. S. Catalin Ionescu, Fuxin Li, “Latent structured models for human pose estimation,” in International Conference on Computer Vision , 2011
2011
Earlier work this paper cites.
V. Ramakrishna, T. Kanade, and Y. Sheikh, “Reconstructing 3d human pose from 2d image landmarks,” in European Conference on Computer Vision . Springer, 2012, pp. 573–586
2012
Earlier work this paper cites.
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 7, pp. 1325–1339, 2014
2014
Earlier work this paper cites.
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, “2d human pose estimation: New benchmark and state of the art analysis,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2014
2014
Earlier work this paper cites.
S. Li and A. B. Chan, “3d human pose estimation from monocular images with deep convolutional neural network,” in Asian Conference on Computer Vision . Springer, 2014, pp. 332–347
2014
Earlier work this paper cites.
I. Akhter and M. J. Black, “Pose-conditioned joint angle limits for 3d human pose reconstruction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1446–1455
2015
Earlier work this paper cites.
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “Smpl: A skinned multi-person linear model,” ACM Transactions on Graphics (TOG) , vol. 34, no. 6, p. 248, 2015
2015
Earlier work this paper cites.
H. Yonemoto, K. Murasaki, T. Osawa, K. Sudo, J. Shimamura, and Y. Taniguchi, “Egocentric articulated pose tracking for action recognition,” in International Conference on Machine Vision Applications (MVA) , 2015
2015
Earlier work this paper cites.
G. Rogez, J. S. Supancic, and D. Ramanan, “First-person pose recognition using egocentric workspaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4325–4333
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
U. Hess, K. Kafetsios, H. Mauersberger, C. Blaison, and C.-L. Kessler, “Signal and noise in the perception of facial emotion expressions: From labs to life,” Personality and Social Psychology Bulletin , vol. 42, no. 8, pp. 1092–1110, 2016
2016
Earlier work this paper cites.
S. Park, J. Hwang, and N. Kwak, “3d human pose estimation using convolutional neural networks with 2d pose information,” in European Conference on Computer Vision, Workshops . Springer, 2016, pp. 156–169
2016
Earlier work this paper cites.
M. S. V. L. Bugra Tekin, Isinsu Katircioglu and P. Fua, “Structured prediction of 3d human pose with deep neural networks,” in Proceedings of the British Machine Vision Conference (BMVC) , E. R. H. Richard C. Wilson and W. A. P. Smith, Eds. BMVA Press, September 2016, pp. 130.1–130.11. [Online]. Available: https://dx.doi.org/10.5244/C.30.130
2016
Earlier work this paper cites.
X. Zhou, X. Sun, W. Zhang, S. Liang, and Y. Wei, “Deep kinematic pose regression,” in European Conference on Computer Vision . Springer, 2016, pp. 186–201
2016
Earlier work this paper cites.
X. Zhou, M. Zhu, S. Leonardos, K. G. Derpanis, and K. Daniilidis, “Sparseness meets deepness: 3d human pose estimation from monocular video,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4966–4975
2016
Earlier work this paper cites.
F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero, and M. J. Black, “Keep it smpl: Automatic estimation of 3d human pose and shape from a single image,” in European Conference on Computer Vision . Springer, 2016, pp. 561–578
2016
Earlier work this paper cites.
M. Sanzari, V. Ntouskos, and F. Pirri, “Bayesian image based 3d pose estimation,” in European Conference on Computer Vision . Springer, 2016, pp. 566–582
2016
Earlier work this paper cites.
G. Rogez and C. Schmid, “Mocap-guided data augmentation for 3d pose estimation in the wild,” in Advances in Neural Information Processing Systems , 2016, pp. 3108–3116
2016
Earlier work this paper cites.
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, “Convolutional pose machines,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4724–4732
2016
Earlier work this paper cites.
A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European Conference on Computer Vision . Springer, 2016, pp. 483–499
2016
Earlier work this paper cites.
L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. V. Gehler, and B. Schiele, “Deepcut: Joint subset partition and labeling for multi person pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4929–4937
2016
Earlier work this paper cites.
J. Wu, T. Xue, J. J. Lim, Y. Tian, J. B. Tenenbaum, A. Torralba, and W. T. Freeman, “Single image 3d interpreter network,” in European Conference on Computer Vision . Springer, 2016, pp. 365–382
2016
Earlier work this paper cites.
M. Ma, H. Fan, and K. M. Kitani, “Going deeper into first-person activity recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1894–1903, 2016
2016
Cited alongside, same era.
H. Rhodin, C. Richardt, D. Casas, E. Insafutdinov, M. Shafiei, H.-P. Seidel, B. Schiele, and C. Theobalt, “Egocap: egocentric marker-less motion capture with two fisheye cameras,” ACM Transactions on Graphics (TOG) , vol. 35, no. 6, p. 162, 2016
2016
Cited alongside, same era.
T. von Marcard, G. Pons-Moll, and B. Rosenhahn, “Human pose estimation from video and imus,” Transactions on Pattern Analysis and Machine Intelligence (PAMI) , jan 2016
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
D. Drover, C.-H. Chen, A. Agrawal, A. Tyagi, and C. Phuoc Huynh, “Can 3d pose be learned from 2d projections alone?” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 0–0
2018
Later among the works it cites.
G. Pavlakos, L. Zhu, X. Zhou, and K. Daniilidis, “Learning to estimate 3d human pose and shape from a single color image,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
H. Rhodin, M. Salzmann, and P. Fua, “Unsupervised geometry-aware representation for 3d human pose estimation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 750–767
2018
Later among the works it cites.
M. Omran, C. Lassner, G. Pons-Moll, P. Gehler, and B. Schiele, “Neural body fitting: Unifying deep learning and model based human pose and shape estimation,” in International Conference on 3D Vision (3DV) , sep 2018
2018
Later among the works it cites.
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H. Yasin, U. Iqbal, B. Kruger, A. Weber, and J. Gall, “A dual-source approach for 3d pose estimation from a single image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4948–4956
2016
Cited alongside, same era.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2016
Cited alongside, same era.
G. Pavlakos, X. Zhou, K. G. Derpanis, and K. Daniilidis, “Coarse-to-fine volumetric prediction for single-image 3d human pose,” in Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on . IEEE, 2017, pp. 1263–1272
2017
Cited alongside, same era.
D. Mehta, H. Rhodin, D. Casas, P. Fua, O. Sotnychenko, W. Xu, and C. Theobalt, “Monocular 3d human pose estimation in the wild using improved cnn supervision,” in 2017 International Conference on 3D Vision (3DV) . IEEE, 2017, pp. 506–516
2017
Cited alongside, same era.
J. Martinez, R. Hossain, J. Romero, and J. J. Little, “A simple yet effective baseline for 3d human pose estimation,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2017
2017
Cited alongside, same era.
F. Moreno-Noguer, “3d human pose estimation from a single image via distance matrix regression,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2017, pp. 1561–1570
2017
Cited alongside, same era.
X. Zhou, M. Zhu, S. Leonardos, and K. Daniilidis, “Sparse representation for 3d shape estimation: A convex relaxation approach,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 8, pp. 1648–1661, 2017
2017
Cited alongside, same era.
G. Varol, J. Romero, X. Martin, N. Mahmood, M. J. Black, I. Laptev, and C. Schmid, “Learning from synthetic humans,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) , 2017
2017
Cited alongside, same era.
A. Kanazawa, M. J. Black, D. W. Jacobs, and J. Malik, “End-to-end recovery of human shape and pose,” in Computer Vision and Pattern Regognition (CVPR) , 2018
2018
Later among the works it cites.
Y. Yuan and K. Kitani, “3d ego-pose estimation via imitation learning,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 735–750
2018
Later among the works it cites.
M. Amer, S. V. Amer, and A. Maria, “Deep 3d human pose estimation under partial body presence,” in Proceedings of the IEEE International Conference on Image Processing (ICIP) , 2018
2018
Later among the works it cites.
Y. Huang, M. Kaufmann, E. Aksan, M. J. Black, O. Hilliges, and G. Pons-Moll, “Deep inertial poser learning to reconstruct human pose from sparseinertial measurements in real time,” ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) , vol. 37, no. 6, pp. 185:1–185:15, nov 2018
2018
Later among the works it cites.
T. von Marcard, R. Henschel, M. Black, B. Rosenhahn, and G. Pons-Moll, “Recovering accurate 3d human pose in the wild using imus and a moving camera,” in European Conference on Computer Vision (ECCV) , sep 2018
2018
Later among the works it cites.
M. R. I. Hossain and J. J. Little, “Exploiting temporal information for 3d human pose estimation,” in European Conference on Computer Vision . Springer, 2018, pp. 69–86
2018
Later among the works it cites.
A. Kanazawa, M. J. Black, D. W. Jacobs, and J. Malik, “End-to-end recovery of human shape and pose,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7122–7131
2018
Later among the works it cites.
X. Zhou, M. Zhu, G. Pavlakos, S. Leonardos, K. G. Derpanis, and K. Daniilidis, “Monocap: Monocular human motion capture using a cnn coupled with a geometric prior,” IEEE transactions on pattern analysis and machine intelligence , 2018
2018
Later among the works it cites.
H. Fang, Y. Xu, W. Wang, X. Liu, and S. Zhu, “Learning pose grammar to encode human body configuration for 3d pose estimation,” in AAAI , 2018
2018
Later among the works it cites.
https://medium.com/@DeepMotionInc/how-to-make-3-point-tracked-full-body-avatars-in-vr-34b3f6709782. (last accessed on 2019-03-19) How to make 3 point tracked full-body avatars in vr, https://medium.com/@deepmotioninc/how-to-make-3-point-tracked-full-body-avatars-in-vr-34b3f6709782. [Online]. Available: https://medium.com/@DeepMotionInc/how-to-make-3-point-tracked-full-body-avatars-in-vr-34b3f6709782
2019
Later among the works it cites.
G. Rogez, P. Weinzaepfel, and C. Schmid, “Lcr-net++: Multi-person 2d and 3d pose detection in natural images,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 5, pp. 1146–1161, 2019
2019
Later among the works it cites.
W. Xu, A. Chatterjee, M. Zollhoefer, H. Rhodin, P. Fua, H.-P. Seidel, and C. Theobalt, “Mo 2
2019
Later among the works it cites.
G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3d hands, face, and body from a single image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 975–10 985
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Kanazawa, J. Y. Zhang, P. Felsen, and J. Malik, “Learning 3d human dynamics from video,” in Computer Vision and Pattern Regognition (CVPR) , 2019
2019
Later among the works it cites.
T. Alldieck, M. Magnor, B. L. Bhatnagar, C. Theobalt, and G. Pons-Moll, “Learning to reconstruct people in clothing from a single RGB camera,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , jun 2019
2019
Later among the works it cites.
B. L. Bhatnagar, G. Tiwari, C. Theobalt, and G. Pons-Moll, “Multi-garment net: Learning to dress 3d people from images,” in IEEE International Conference on Computer Vision (ICCV) . IEEE, oct 2019
2019
Later among the works it cites.
——, “Ego-pose estimation and forecasting as real-time pd control,” in The IEEE International Conference on Computer Vision (ICCV) , October 2019
2019
Later among the works it cites.
D. Tome, P. Peluse, L. Agapito, and H. Badino, “xr-egopose: Egocentric 3d human pose from an hmd camera,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 7728–7738
2019
Later among the works it cites.
C. Malleson, J. Collomosse, and A. Hilton, “Real-time multi-person motion capture from multi-view video and imus,” International Journal of Computer Vision , pp. 1–18, 2019
2019
Later among the works it cites.
https://www.mixamo.com/. (last accessed on 2019-03-19) Animated 3d characters. [Online]. Available: https://www.mixamo.com/
2019
Later among the works it cites.
Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5745–5753
2019
Later among the works it cites.
K. He, R. Girshick, and P. Dollár, “Rethinking imagenet pre-training,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 4918–4927
2019
Later among the works it cites.