Fetching the paper…
Reading the bibliography…
Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames.
Tempo, R., Bai, E.W., Dabbene, F.: Probabilistic robustness analysis: explicit bounds for the minimum number of samples. In: Proceedings of 35th IEEE Conference on Decision and Control. vol. 3, pp. 3424–3428 vol.3 (Dec 1996)
1996
Earlier work this paper cites.
Park, S., Trivedi, M.M.: Understanding human interactions with track and body synergies (tbs) captured from multiple views. Computer Vision and Image Understanding 111
2008
Earlier work this paper cites.
Lin, H.Y., Chen, T.W.: Augmented reality with human body interaction based on monocular 3d pose estimation. In: International Conference on Advanced Concepts for Intelligent Vision Systems. pp. 321–331. Springer (2010)
2010
Earlier work this paper cites.
Park, D., Ramanan, D.: N-best maximal decoders for part models. In: 2011 International Conference on Computer Vision. pp. 2627–2634. IEEE (2011)
2011
Earlier work this paper cites.
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR 2011. pp. 1297–1304. Ieee (2011)
2011
Earlier work this paper cites.
Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR 2011. pp. 1385–1392 (June 2011). https://doi.org/10.1109/CVPR.2011.5995741
2011
Earlier work this paper cites.
Cristani, M., Raghavendra, R., Del Bue, A., Murino, V.: Human behavior analysis in video surveillance: A social signal processing perspective. Neurocomputing 100
2013
Earlier work this paper cites.
Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: International Conf. on Computer Vision (ICCV). pp. 3192–3199 (Dec 2013)
2013
Earlier work this paper cites.
Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.: Strong appearance and expressive spatial models for human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3487–3494 (2013)
2013
Earlier work this paper cites.
Zhang, W., Zhu, M., Derpanis, K.G.: From actemes to action: A strongly-supervised representation for detailed action understanding. In: 2013 IEEE International Conference on Computer Vision. pp. 2248–2255 (Dec 2013). https://doi.org/10.1109/ICCV.2013.280
2013
Earlier work this paper cites.
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 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.
Chen, X., Yuille, A.L.: Articulated pose estimation by a graphical model with image dependent pairwise relations. In: Advances in Neural Information Processing Systems. pp. 1736–1744 (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems. pp. 568–576 (2014)
2014
Earlier work this paper cites.
Toshev, A., Szegedy, C.: Deeppose: Human pose estimation via deep neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1653–1660 (June 2014). https://doi.org/10.1109/CVPR.2014.214
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Pfister, T., Charles, J., Zisserman, A.: Flowing convnets for human pose estimation in videos. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1913–1921 (2015)
2015
Cited alongside, same era.
Xiaohan Nie, B., Xiong, C., Zhu, S.C.: Joint action recognition and pose estimation from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1293–1301 (2015)
2015
Cited alongside, same era.
Charles, J., Pfister, T., Magee, D., Hogg, D., Zisserman, A.: Personalizing human video pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3063–3072 (2016)
2016
Cited alongside, same era.
Chu, X., Ouyang, W., Li, H., Wang, X.: Structured feature learning for pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4715–4723 (2016)
2016
Cited alongside, same era.
2017
Later among the works it cites.
Song, J., Wang, L., Van Gool, L., Hilliges, O.: Thin-slicing network: A deep structured model for pose estimation in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4220–4229 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gkioxari, G., Toshev, A., Jaitly, N.: Chained predictions using convolutional neural networks. In: European Conference on Computer Vision. pp. 728–743. Springer (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4724–4732 (2016)
2016
Cited alongside, same era.
Yang, W., Ouyang, W., Li, H., Wang, X.: End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3073–3082 (2016)
2016
Cited alongside, same era.
Belagiannis, V., Zisserman, A.: Recurrent human pose estimation. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). pp. 468–475. IEEE (2017)
2017
Cited alongside, same era.
2018
Later among the works it cites.
Luo, Y., Ren, J., Wang, Z., Sun, W., Pan, J., Liu, J., Pang, J., Lin, L.: LSTM pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5207–5215 (2018)
2018
Later among the works it cites.
Nie, X., Feng, J., Yan, S.: Mutual learning to adapt for joint human parsing and pose estimation. In: ECCV (2018)
2018
Later among the works it cites.
Tang, W., Yu, P., Wu, Y.: Deeply learned compositional models for human pose estimation. In: The European Conference on Computer Vision (ECCV) (September 2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
Bertasius, G., Feichtenhofer, C., Tran, D., Shi, J., Torresani, L.: Learning temporal pose estimation from sparsely-labeled videos. In: Wallach, H., Larochelle, H., Beygelzimer, A., dÁlché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 3027–3038. Curran Associates, Inc. (2019), http://papers.nips.cc/paper/8567-learning-temporal-pose-estimation-from-sparsely-labeled-videos.pdf
2019
Later among the works it cites.
Kreiss, S., Bertoni, L., Alahi, A.: Pifpaf: Composite fields for human pose estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.
Nie, X., Li, Y., Luo, L., Zhang, N., Feng, J.: Dynamic kernel distillation for efficient pose estimation in videos. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.
Yang, J., Shen, X., Xing, J., Tian, X., Li, H., Deng, B., Huang, J., Hua, X.s.: Quantization networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.
Zhang, F., Zhu, X., Ye, M.: Fast human pose estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.