Abstract
We describe a method for estimating parametric 3D models of the human body from RGB-D sensor scans. We estimate both the pose and shape of the body. Our method uses a minimization function that relies on distance calculations between points selected using nearest neighbors and angles between normals. In addition, the use of intermediate templates helps to speed up and improve the accuracy of the minimization. The method has been tested on three datasets containing scans of both male and female subjects. Results show that the method estimates parametric models that closely resemble the original model.
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
Similar content being viewed by others
Notes
References
Leroy, V., Franco, J.-S., Boyer, E.: Multi-view dynamic shape refinement using local temporal integration, pp. 3113–3122, October 2017
Yang, J., Franco, J.-S., Hétroy-Wheeler, F., Wuhrer, S.: Estimation of human body shape in motion with wide clothing. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 439–454. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_27
Varol, G., et al.: Learning from synthetic humans. In: CVPR (2017)
Xu, H., Bazavan, E.G., Zanfir, A., Freeman, B., Sukthankar, R., Sminchisescu, C.:. GHUM & GHUML: generative 3D human shape and articulated pose models. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (Oral), pp. 6184–6193 (2020)
Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M.J., Gehler, P.V.: Unite the people: closing the loop between 3D and 2D human representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 2017
Pishchulin, L., Wuhrer, S., Helten, T., Theobalt, C., Schiele, B.: Building statistical shape spaces for 3D human modeling. Pattern Recognit. 67, 276–286 (2017)
Tamás, P., Halasz, M.: Human body measuring and 3D modelling, January 2009
Yan, S., Wirta, J., Kämäräinen, J.-K.: Anthropometric clothing measurements from 3D body scans (2019)
Ma, Q., et al.: Learning to dress 3D people in generative clothing. In: Computer Vision and Pattern Recognition (CVPR), June 2020
Fuster-Guilló, A., Azorín-López, J., Saval-Calvo, M., Castillo-Zaragoza, J.M., Garcia-DUrso, N., Fisher, R.B.: RGB-D-based framework to acquire, visualize and measure the human body for dietetic treatments. Sensors 20(13), 3690 (2020)
Prokudin, S., Lassner, C., Romero, J.: Efficient learning on point clouds with basis point sets. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4332–4341 (2019)
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 34(6), 248:1–248:16 (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
Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields (2019)
Kolotouros, N., Pavlakos, G., Black, M.J., Daniilidis, K.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop (2019)
Kocabas, M., Athanasiou, N., Black, M.J.: VIBE: video inference for human body pose and shape estimation (2020)
Saito, S., Yang, J., Ma, Q., Black, M.J.: SCANimate: weakly supervised learning of skinned clothed avatar networks (2021)
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016)
von Marcard, T., Pons-Moll, G., Rosenhahn, B.: Human pose estimation from video and IMUs. Trans. Pattern Anal. Mach. Intell. 38(8), 1533–1547 (2016)
Acknowledgment
This work was supported by the Spanish State Research Agency (AEI) under grant PID2020-119144RB-I00 funded by MCIN/AEI/10.13039/501100011033, and has also been developed with the support of valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, co-funded by the European Union.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Garcia-D’Urso, N.E., Azorin-Lopez, J., Fuster-Guillo, A. (2023). Accurate Estimation of Parametric Models of the Human Body from 3D Point Clouds. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-42536-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42535-6
Online ISBN: 978-3-031-42536-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)