[6] Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, Chia-Chih Chen, Jong Taek Lee, Saurjit Mukherjee, JK Aggarwal, Hyungtae Lee, Larry Davis, et al. A large-scale benchmark dataset for event recognition in surveillance video. In Conference on Computer Vision and Pattern Recognition, 2011.
[7] Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. Social lstm: Human trajectory prediction in crowded spaces. In Conference on Computer Vision and Pattern Recognition, 2016.
[8] Lukasz Kidziński, Bryan Yang, Jennifer L Hicks, Apoorva Rajagopal, Scott L Delp, and Michael H Schwartz. Deep neural networks enable quantitative movement analysis using single-camera videos. Nature communications, 2020.
[9] Caroline J Charpentier and John P O'Doherty. The application of computational models to social neuroscience: promises and pitfalls. Social neuroscience, 2018.
[10] Cigdem Bayan, Vittorio Murino, Gentiane Venture, and Agnieszka Wykowska. Computational Approaches for Human-Human and Human-Robot Social Interactions. Frontiers in Robotics and AI, 2020.
[11] Leyla Isik, Anna Mynick, Dimitrios Pantazis, and Nancy Kanwisher. The speed of human social interaction perception. NeuroImage, 2020.
[12] Hanbyul Joo, Hao Liu, Lei Tan, Lin Gui, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, and Yaser Sheikh. Panoptic Studio: A Massively Multiview System for Social Motion Capture. In International Conference on Computer Vision, 2015.
[13] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. *Smpl: A skinned multi-person linear model. Transactions on Graphics*, 2015.
[14] Ruilong Li, Shan Yang, David A Ross, and Angjoo Kanazawa. Ai choreographer: Music conditioned 3d dance generation with aist++. In International Conference on Computer Vision, 2021.
[15] Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, and Michael J. Black. AMASS: Archive of motion capture as surface shapes. In International Conference on Computer Vision, 2019.
[16] Bharat Lal Bhatnagar, Xianghui Xie, Ilya A Petrov, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Behave: Dataset and method for tracking human object interactions. In Conference on Computer Vision and Pattern Recognition, 2022.
[17] Nan Jiang, Tengyu Liu, Zhexuan Cao, Jieming Cui, Yixin Chen, He Wang, Yixin Zhu, and Siyuan Huang. Chairs: Towards full-body articulated human-object interaction. arXiv preprint arXiv:2212.10621, 2022.
[18] Alessio Sampieri, Guido D'Amely, Andrea Avogaro, Federico Cunico, Geri Skenderi, Francesco Setti, Marco Cristani, and Fabio Galasso. Pose forecasting in industrial human-robot collaboration. European Conference on Computer Vision, 2022.
[19] Yang Zheng, Yanchao Yang, Kaichun Mo, Jiaman Li, Tao Yu, Yebin Liu, C Karen Liu, and Leonidas J Guibas. Gimo: Gaze-informed human motion prediction in context. In European Conference on Computer Vision, 2022.
[20] Zhe Cao, Hang Gao, Karttikeya Mangalam, Qi-Zhi Cai, Minh Vo, and Jitendra Malik. Long-term human motion prediction with scene context. In European Conference on Computer Vision, 2020.
[21] Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. Pattern Analysis and Machine Intelligence, 2014.
[22] Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Wei Liang, and Siyuan Huang. Humanise: Language-conditioned human motion generation in 3d scenes. In Advances in Neural Information Processing Systems, 2022.