Abstract
This work addresses the problem of 3D human body shape and pose estimation from a single depth image. Most 3D human pose estimation methods based on deep learning utilize RGB images instead of depth images. Traditional optimization-based methods using depth images aim to establish point correspondences between the depth images and the template model. In this paper, we propose a novel method to estimate the 3D pose and shape of a human body from depth images. Specifically, based on the joints features and original depth features, we propose a spatial attention feature extractor to capture spatial local features of depth images and 3D joints by learning dynamic weights of the features. In addition, we generalize our method to real depth data through a weakly-supervised method. We conduct extensive experiments on SURREAL, Human3.6M, DFAUST, and real depth images of human bodies. The experimental results demonstrate that our 3D human pose estimation method can yield good performance.
This work was supported by the Natural Science Foundation of China under Grant Nos. 61602444, U1713208, and Program for Changjiang Scholars.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7122–7131, June 2018
Varol, G., et al.: Learning from synthetic humans. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 109–117, July 2017
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2014)
Bogo, F., Romero, J., Pons-Moll, G., Black, M.J.: Dynamic faust: registering human bodies in motion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6233–6242, July 2017
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. 34, 1–16 (2015)
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 561–578. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_34
Huang, Y., et al.: Towards accurate marker-less human shape and pose estimation over time. In: 2017 International Conference on 3D Vision (3DV), pp. 421–430. IEEE (2017)
Kolotouros, N., Pavlakos, G., Daniilidis, K.: Convolutional mesh regression for single-image human shape reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4501–4510 (2019)
Wei, L., Huang, Q., Ceylan, D., Vouga, E., Li, H.: Dense human body correspondences using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1544–1553 (2016)
Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., Padoy, N.: A multi-view RGB-D approach for human pose estimation in operating rooms. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 363–372. IEEE (2017)
Pavlakos, G., Zhou, X., Daniilidis, K.: Ordinal depth supervision for 3D human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7307–7316 (2018)
Li, R., Cai, C., Georgakis, G., Karanam, S., Chen, T., Wu, Z.: Towards robust RGB-D human mesh recovery. arXiv preprint arXiv:1911.07383 (2019)
Kato, H., Ushiku, Y., Harada, T.: Neural 3D mesh renderer. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3907–3916 (2018)
Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 536–553. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_33
Loper, M., Mahmood, N., Black, M.J.: MoSh: motion and shape capture from sparse markers. ACM Trans. Graph. (TOG) 33(6), 1–13 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Yu, R., Saito, S., Li, H., Ceylan, D., Li, H.: Learning dense facial correspondences in unconstrained images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4723–4732 (2017)
Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2252–2261 (2019)
Zhu, H., Zuo, X., Wang, S., Cao, X., Yang, R.: Detailed human shape estimation from a single image by hierarchical mesh deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4491–4500 (2019)
Guo, K., Xu, F., Wang, Y., Liu, Y., Dai, Q.: Robust non-rigid motion tracking and surface reconstruction using L0 regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3083–3091 (2015)
Vlasic, D., Baran, I., Matusik, W., Popović, J.: Articulated mesh animation from multi-view silhouettes. In: ACM SIGGRAPH 2008 papers, pp. 1–9 (2008)
Zhang, C., Pujades, S., Black, M.J., Pons-Moll, G.: Detailed, accurate, human shape estimation from clothed 3D scan sequences. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4191–4200
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, L., Wang, K., Yang, J. (2020). 3D Human Body Shape and Pose Estimation from Depth Image. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-60633-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60632-9
Online ISBN: 978-3-030-60633-6
eBook Packages: Computer ScienceComputer Science (R0)