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Understanding 3d human interactions is fundamental for fine-grained scene analysis and behavioural modeling.
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2009
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L. Sigal, A. O. Balan, and M. J. Black, “HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion,” IJCV , 2010
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2011
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2012
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2012
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2013
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2013
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2014
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M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “SMPL: A skinned multi-person linear model,” (SIGGRAPH) , vol. 34, no. 6, pp. 248:1–16, 2015
2015
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J. T. Suvilehto, E. Glerean, R. I. M. Dunbar, R. Hari, and L. Nummenmaa, “Topography of social touching depends on emotional bonds between humans,” Proceedings of the National Academy of Sciences , vol. 112, no. 45, pp. 13 811–13 816, 2015. [Online]. Available: https://www.pnas.org/content/112/45/13811
2015
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S. Kalkowski, C. Schulze, A. Dengel, and D. Borth, “Real-time analysis and visualization of the yfcc100m dataset,” in Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions . ACM, 2015
2015
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D. Tzionas, L. Ballan, A. Srikantha, P. Aponte, M. Pollefeys, and J. Gall, “Capturing hands in action using discriminative salient points and physics simulation,” IJCV , 2016
2016
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H. Salam, O. Celiktutan, I. Hupont, H. Gunes, and M. Chetouani, “Fully automatic analysis of engagement and its relationship to personality in human-robot interactions,” IEEE Access , vol. 5, pp. 705–721, 2016
2016
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C. Leclère, M. Avril, S. Viaux, N. Bodeau, C. Achard, S. Missonnier, M. Keren, M. Chetouani, and D. Cohen, “Interaction and behaviour imaging: A novel method to measure mother-infant interaction using video 3d reconstruction,” Translational Psychiatry , vol. 6, 05 2016
2016
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B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m: The new data in multimedia research,” Communications of the ACM , vol. 59, no. 2, pp. 64–73, 2016
2016
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E. Marinoiu, D. Papava, and C. Sminchisescu, “Pictorial Human Spaces: A Computational Study on the Human Perception of 3D Articulated Poses,” in IJCV , February 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
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A.-I. Popa, M. Zanfir, and C. Sminchisescu, “Deep multitask architecture for integrated 2d and 3d human sensing,” in CVPR , 2017
2017
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J. Martinez, R. Hossain, J. Romero, and J. J. Little, “A simple yet effective baseline for 3d human pose estimation,” in ICCV , 2017
2017
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J. Taylor, V. Tankovich, D. Tang, C. Keskin, D. Kim, P. Davidson, A. Kowdle, and S. Izadi, “Articulated distance fields for ultra-fast tracking of hands interacting,” ACM Trans. Graph. , vol. 36, no. 6, pp. 244:1–244:12, Nov. 2017. [Online]. Available: http://doi.acm.org/10.1145/3130800.3130853
2017
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T.-H. Pham, N. Kyriazis, A. Argyros, and A. Kheddar, “Hand-object contact force estimation from markerless visual tracking,” PAMI , 2017
K. Su, D. Yu, Z. Xu, X. Geng, and C. Wang, “Multi-person pose estimation with enhanced channel-wise and spatial information,” in CVPR , 2019
2019
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2019
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N. Kolotouros, G. Pavlakos, M. J. Black, and K. Daniilidis, “Learning to reconstruct 3D human pose and shape via model-fitting in the loop,” in ICCV , 2019
2019
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F. Mueller, M. Davis, F. Bernard, O. Sotnychenko, M. Verschoor, M. A. Otaduy, D. Casas, and C. Theobalt, “Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera,” ACM Transactions on Graphics (TOG) , vol. 38, no. 4, 2019
2019
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2017
Cited alongside, same era.
O. Rudovic, J. Lee, L. Mascarell-Maricic, B. W. Schuller, and R. W. Picard, “Measuring engagement in robot-assisted autism therapy: A cross-cultural study,” Frontiers in Robotics and AI , 2017
2017
Cited alongside, same era.
O. Celiktutan, E. Skordos, and H. Gunes, “Multimodal human-human-robot interactions (mhhri) dataset for studying personality and engagement,” IEEE Transactions on Affective Computing , 2017
2017
Cited alongside, same era.
J. Romero, D. Tzionas, and M. J. Black, “Embodied hands: Modeling and capturing hands and bodies together,” SIGGRAPH Asia , 2017. [Online]. Available: http://doi.acm.org/10.1145/3130800.3130883
2017
Cited alongside, same era.
D. C. Luvizon, D. Picard, and H. Tabia, “2d/3d pose estimation and action recognition using multitask deep learning,” in CVPR , 2018, pp. 5137–5146
2018
Cited alongside, same era.
W. Yang, W. Ouyang, X. Wang, J. Ren, H. Li, and X. Wang, “3d human pose estimation in the wild by adversarial learning,” in CVPR , 2018
2018
Cited alongside, same era.
H. Joo, T. Simon, and Y. Sheikh, “Total capture: A 3d deformation model for tracking faces, hands, and bodies,” in CVPR , 2018
2018
Cited alongside, same era.
D. Mehta, O. Sotnychenko, F. Mueller, W. Xu, S. Sridhar, G. Pons-Moll, and C. Theobalt, “Single-shot multi-person 3D pose estimation from monocular RGB,” in 3DV , 2018
2018
Cited alongside, same era.
M. Hassan, V. Choutas, D. Tzionas, and M. J. Black, “Resolving 3D human pose ambiguities with 3D scene constraints,” in ICCV , 2019. [Online]. Available: https://prox.is.tue.mpg.de
2019
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S. Brahmbhatt, C. Ham, C. C. Kemp, and J. Hays, “Contactdb: Analyzing and predicting grasp contact via thermal imaging,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8709–8719
2019
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N. Kolotouros, G. Pavlakos, and K. Daniilidis, “Convolutional mesh regression for single-image human shape reconstruction,” in CVPR , 2019
2019
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M. Kocabas, N. Athanasiou, and M. J. Black, “VIBE: Video inference for human body pose and shape estimation,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) . Piscataway, NJ: IEEE, Jun. 2020, pp. 5252–5262
2020
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M. Fieraru, M. Zanfir, E. Oneata, A.-I. Popa, V. Olaru, and C. Sminchisescu, “Three-dimensional reconstruction of human interactions,” in The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
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H. Xu, E. G. Bazavan, A. Zanfir, W. T. Freeman, R. Sukthankar, and C. Sminchisescu, “Ghum & ghuml: Generative 3d human shape and articulated pose models,” in CVPR , 2020
2020
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A. Zanfir, E. G. Bazavan, H. Xu, W. T. Freeman, R. Sukthankar, and C. Sminchisescu, “Weakly supervised 3d human pose and shape reconstruction with normalizing flows,” in European Conference on Computer Vision . Springer, 2020, pp. 465–481
2020
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W. Jiang, N. Kolotouros, G. Pavlakos, X. Zhou, and K. Daniilidis, “Coherent reconstruction of multiple humans from a single image,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 5579–5588
2020
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M. Fieraru, M. Zanfir, T. Szente, E. Bazavan, V. Olaru, and C. Sminchisescu, “Remips: Physically consistent 3d reconstruction of multiple interacting people under weak supervision,” in Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34. Curran Associates, Inc., 2021, pp. 19 385–19 397. [Online]. Available: https://proceedings.neurips.cc/paper/2021/file/a1a2c3fed88e9b3ba5bc3625c074a04e-Paper.pdf
2021
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W. Guo, E. Corona, F. Moreno-Noguer, and X. Alameda-Pineda, “Pi-net: Pose interacting network for multi-person monocular 3d pose estimation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 2796–2806
2021
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M. Fieraru, M. Zanfir, E. Oneata, A.-I. Popa, V. Olaru, and C. Sminchisescu, “Learning complex 3d human self-contact,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 2, 2021, pp. 1343–1351
2021
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L. Muller, A. A. A. Osman, S. Tang, C.-H. P. Huang, and M. J. Black, “On self-contact and human pose,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 9990–9999
2021
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M. Zanfir, A. Zanfir, E. G. Bazavan, W. T. Freeman, R. Sukthankar, and C. Sminchisescu, “Thundr: Transformer-based 3d human reconstruction with markers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 971–12 980
2021
Later among the works it cites.
M. Fieraru, M. Zanfir, T. Szente, E. Bazavan, V. Olaru, and C. Sminchisescu, “Remips: Physically consistent 3d reconstruction of multiple interacting people under weak supervision,” in Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34. Curran Associates, Inc., 2021, pp. 19 385–19 397. [Online]. Available: https://proceedings.neurips.cc/paper/2021/file/a1a2c3fed88e9b3ba5bc3625c074a04e-Paper.pdf
2021
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W. Guo, X. Bie, X. Alameda-Pineda, and F. Moreno-Noguer, “Multi-person extreme motion prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 13 053–13 064
2022
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Z. Li, J. Liu, Z. Zhang, S. Xu, and Y. Yan, “Cliff: Carrying location information in full frames into human pose and shape estimation,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part V . Springer, 2022, pp. 590–606
2022
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Y. Yin, C. Guo, M. Kaufmann, J. J. Zarate, J. Song, and O. Hilliges, “Hi4d: 4d instance segmentation of close human interaction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 016–17 027
2023
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