Cited by 2523D Shape Modeling and AnalysisHuman Pose and Action RecognitionHuman Motion and Animation

STAR: Sparse Trained Articulated Human Body Regressor

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Osman, Ahmed A. A., Bolkart, Timo, Black, Michael J.·2020·Lecture Notes in Computer Science

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  1. [3]3. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Detailed human avatars from monocular video. In: International Conference on 3D Vision (3DV). pp. 98-109 (2018)
  2. [4]4. Allen, B., Curless, B., Popovi´ c, Z.: Articulated body deformation from range scan data. ACM Transactions on Graphics, (Proc. SIGGRAPH) 21 (3), 612-619 (2002)
  3. [5]5. Allen, B., Curless, B., Popovi´ c, Z.: The space of human body shapes: Reconstruction and parameterization from range scans. ACM Transactions on Graphics, (Proc. SIGGRAPH) 22 (3), 587-594 (2003)
  4. [7]7. Chen, Y., Liu, Z., Zhang, Z.: Tensor-based human body modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 105-112 (2013)
  5. [8]8. Freifeld, O., Black, M.J.: Lie Bodies: A manifold representation of 3D human shape. In: European Conference on Computer Vision (ECCV). pp. 1-14 (2012)
  6. [9]9. Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.P.: A statistical model of human pose and body shape. Computer Graphics Forum 28 (2), 337-346 (2009)
  7. [10]10. Hirshberg, D., Loper, M., Rachlin, E., Black, M.: Coregistration: Simultaneous alignment and modeling of articulated 3D shape. In: European Conference on Computer Vision (ECCV). pp. 242-255 (2012)
  8. [11]11. Huang, Y., Bogo, F., Lassner, C., Kanazawa, A., Gehler, P.V., Romero, J., Akhter, I., Black, M.J.: Towards accurate marker-less human shape and pose estimation over time. In: International Conference on 3D Vision (3DV). pp. 421-430 (2017)
  9. [13]13. Jacobson, A., Baran, I., Kavan, L., Popovi´ c, J., Sorkine, O.: Fast automatic skinning transformations. ACM Transactions on Graphics (TOG) 31 (4), 1-10 (2012)
  10. [14]14. Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 7122-7131 (2018)
  11. [15]15. Kocabas, M., Athanasiou, N., Black, M.J.: VIBE: Video inference for human body pose and shape estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5253-5263 (2020)
  12. [16]16. 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 (ICCV). pp. 2252-2261 (2019)
  13. [17]17. Kry, P.G., James, D.L., Pai, D.K.: Eigenskin: real time large deformation character skinning in hardware. In: Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation. pp. 153-159 (2002)
  14. [18]18. Kurihara, T., Miyata, N.: Modeling deformable human hands from medical images. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation. pp. 355-363 (2004)
  15. [19]19. Lewis, J.P., Cordner, M., Fong, N.: Pose space deformation: A unified approach to shape interpolation and skeleton-driven deformation. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. pp. 165172. SIGGRAPH '00 (2000)
  16. [20]20. Liu, L., Zheng, Y., Tang, D., Yuan, Y., Fan, C., Zhou, K.: NeuroSkinning: Automatic skin binding for production characters with deep graph networks. ACM Transactions on Graphics (TOG) 38 (4), 1-12 (2019)
  17. [22]22. Loper, M.M., Mahmood, N., Black, M.J.: MoSh: Motion and shape capture from sparse markers. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) 33 (6), 220:1-220:13 (2014)
  18. [23]23. Magnenat-Thalmann, N., Laperrire, R., Thalmann, D.: Joint-dependent local deformations for hand animation and object grasping. In: In Proceedings on Graphics interface. Citeseer (1988)
  19. [25]25. Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: AMASS: Archive of motion capture as surface shapes. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). pp. 5442-5451 (2019)
  20. [26]26. von Marcard, T., Rosenhahn, B., Black, M., Pons-Moll, G.: Sparse inertial poser: Automatic 3D human pose estimation from sparse IMUs. Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics) pp. 349-360 (2017)
  21. [27]27. Neumann, T., Varanasi, K., Wenger, S., Wacker, M., Magnor, M., Theobalt, C.: Sparse localized deformation components. ACM Transactions on Graphics (TOG) 32 (6), 1-10 (2013)
  22. [28]28. Pavlakos, G., Zhu, L., Zhou, X., Daniilidis, K.: Learning to estimate 3D human pose and shape from a single color image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 459-468 (2018)
  23. [30]30. Pons-Moll, G., Romero, J., Mahmood, N., Black, M.J.: Dyna: A model of dynamic human shape in motion. ACM Transactions on Graphics, (Proc. SIGGRAPH) 34 (4), 120:1-120:14 (2015)
  24. [31]31. Rhee, T., Lewis, J.P., Neumann, U.: Real-time weighted pose-space deformation on the gpu. Computer Graphics Forum 25 (3), 439-448 (2006)
  25. [33]33. Rueegg, N., Lassner, C., Black, M.J., Schindler, K.: Chained representation cycling: Learning to estimate 3D human pose and shape by cycling between representations. In: Conference on Artificial Intelligence (AAAI-20) (2020)
  26. [34]34. Saint, A., Ahmed, E., Cherenkova, K., Gusev, G., Aouada, D., Ottersten, B.: 3DBodyTex: Textured 3D body dataset. In: International Conference on 3D Vision (3DV). pp. 495-504 (2018)
  27. [35]35. Seo, H., Cordier, F., Magnenat-Thalmann, N.: Synthesizing animatable body models with parameterized shape modifications. In: ACM SIGGRAPH/Eurographics Symposium on Computer Animation. pp. 120-125. SCA '03 (2003)
  28. [36]36. Tan, J., Budvytis, I., Cipolla, R.: Indirect deep structured learning for 3D human body shape and pose prediction. In: Proceedings of the British Machine Vision Conference (BMVC). pp. 4-7 (2017)
  29. [37]37. Xu, H., Bazavan, E.G., Zanfir, A., Freeman, W.T., Sukthankar, R., Sminchisescu, C.: GHUM & GHUML: Generative 3D human shape and articulated pose models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6184-6193 (2020)
  30. [38]38. Zanfir, A., Marinoiu, E., Sminchisescu, C.: Monocular 3D pose and shape estimation of multiple people in natural scenes-the importance of multiple scene constraints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2148-2157 (2018)

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Authors
Osman, A. A. A., Bolkart, T., & Black, M. J.
Affiliations
Max Planck Institute for Intelligent Systems (DE)
Corresponding author
Osman, Ahmed A. A.
Venue
Lecture Notes in Computer Science
Published
2020
Pages
598-613
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W3053462477
Cited by
252
References
3
Citations by year
2026: 16 · 2025: 41 · 2024: 49 · 2023: 68 · 2022: 47 · 2021: 30 · 2020: 1
FWCI
32.21
Type
conference paper
Language
en
Open access
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Nvidia
  • International Max Planck Research School for Environmental, Cellular and Molecular Microbiology
  • International Max Planck Research School for Advanced Methods in Process and Systems Engineering
Topics
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
Keywords
SMPL, body mass index, 3D human pose estimation, body shape modeling, model parameter reduction, model generalization

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