[37] M. Hassan, D. Ceylan, R. Villegas, J. Saito, J. Yang, Y. Zhou, and M. J. Black, “Stochastic scene-aware motion prediction,” in ICCV, 2021.
[38] M. Hassan, V. Choutas, D. Tzionas, and M. J. Black, “Resolving 3d human pose ambiguities with 3d scene constraints,” in ICCV, 2019.
[39] Y.-L. Li, X. Liu, X. Wu, Y. Li, Z. Qiu, L. Xu, Y. Xu, H.-S. Fang, and C. Lu, "Hake: a knowledge engine foundation for human activity understanding," in TPAMI, 2022.
[40] Y. Zheng, Y. Yang, K. Mo, J. Li, T. Yu, Y. Liu, C. K. Liu, and L. J. Guibas, “Gimo: Gaze-informed human motion prediction in context,” in ECCV, 2022.
[41] C. Guo, X. Zuo, S. Wang, S. Zou, Q. Sun, A. Deng, M. Gong, and L. Cheng, “Action2motion: Conditioned generation of 3d human motions,” in ACM MM, 2020.
[42] R. Gross and J. Shi, "The cmu motion of body (mobo) database," 2001.
[43] C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, "Human3.6M: Large scale datasets and predictive methods for 3d human sensing in natural environments," in TPAMI, 2014.
[44] 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.
[45] M. Trumble, A. Gilbert, C. Malleson, A. Hilton, and J. P. Collomosse, “Total capture: 3d human pose estimation fusing video and inertial sensors,” in BMVC, 2017.
[46] M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, "Smpl: A skinned multiperson linear model," ACM TOG, 2015.
[47] Y. Yuan, J. Song, U. Iqbal, A. Vahdat, and J. Kautz, "Physdiff: Physics-guided human motion diffusion model," in ICCV, 2023.
[48] J. Zhang, Y. Zhang, X. Cun, S. Huang, Y. Zhang, H. Zhao, H. Lu, and X. Shen, "T2m-gpt: Generating human motion from textual descriptions with discrete representations," in CVPR, 2023.
[49] W. Jiao, W. Wang, J.-t. Huang, X. Wang, and Z. Tu, “Is chatgpt a good translator? a preliminary study,” arXiv preprint arXiv:2301.08745, 2023.
[50] R. Daneček, M. J. Black, and T. Bolkart, "Emoca: Emotion driven monocular face capture and animation," in CVPR, 2022.
[51] S. Jin, L. Xu, J. Xu, C. Wang, W. Liu, C. Qian, W. Ouyang, and P. Luo, "Whole-body human pose estimation in the wild," in ECCV, 2020.
[52] L. Xu, S. Jin, W. Liu, C. Qian, W. Ouyang, P. Luo, and X. Wang, “Zoomnas: searching for whole-body human pose estimation in the wild,” TPAMI, 2022.
[53] S. Narasimhaswamy, T. Nguyen, M. Huang, and M. Hoai, “Whose hands are these? hand detection and hand-body association in the wild,” in CVPR, 2022.
[54] A. Savitzky and M. J. Golay, "Smoothing and differentiation of data by simplified least squares procedures," Analytical chemistry, 1964.
[55] A. Zeng, X. Ju, L. Yang, R. Gao, X. Zhu, B. Dai, and Q. Xu, "Deciwatch: A simple baseline for 10x efficient 2d and 3d pose estimation," in ECCV, 2022.
[56] A. Zeng, L. Yang, X. Ju, J. Li, J. Wang, and Q. Xu, "Smoothnet: A plug-and-play network for refining human poses in videos," in ECCV, 2022.
[57] I. Sárándi, A. Hermans, and B. Leibe, "Learning 3d human pose estimation from dozens of datasets using a geometry-aware autoencoder to bridge between skeleton formats," in WACV, 2023.
[58] F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero, and M. J. Black, "Keep it smpl: Automatic estimation of 3d human pose and shape from a single image," in ECCV, 2016.