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Self-adjustable hyper-graphs for video pose estimation based on spatial-temporal subspace construction

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An Erratum to this article was published on 10 August 2021

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References

  1. Moon G, Chang J Y, Lee K M. Posefix: model-agnostic general human pose refinement network. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019

  2. Li Z, Wang X, Wang F, et al. On boosting single-frame 3D human pose estimation via monocular videos. In: Proceedings of IEEE/CVF International Conference on Computer Vision, 2019

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Acknowledgements

This work was supported in part by National Key R&D Program of China (Grant No. 2018YFB-1700603), National Natural Science Foundation of China (Grant Nos. 61672077, 61532002), and Beijing Natural Science Foundation — Haidian Primitive Innovation Joint Fund (Grant No. L182016).

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Jizhou Ma, Shuai Li, Aimin Hao & Qinping Zhao

  2. Department of Computer Science, Stony Brook University, New York, 11794, USA

    Hong Qin

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  1. Jizhou Ma
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  2. Shuai Li
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  3. Hong Qin
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  4. Aimin Hao
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  5. Qinping Zhao
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Correspondence to Shuai Li.

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Ma, J., Li, S., Qin, H. et al. Self-adjustable hyper-graphs for video pose estimation based on spatial-temporal subspace construction. Sci. China Inf. Sci. 65, 139101 (2022). https://doi.org/10.1007/s11432-019-2869-x

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  • DOI: https://doi.org/10.1007/s11432-019-2869-x

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