[24] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[25] Dirk Helbing, Péter Molnár, Illés J Farkas, and Kai Bolay. Self-organizing pedestrian movement. Environment and planning B: planning and design, 28(3):361-383, 2001.
[26] Jonathan Ho and Stefano Ermon. Generative adversarial imitation learning. In Advances in neural information processing systems, pages 4565-4573, 2016.
[27] Zhiyu Huang, Haochen Liu, and Chen Lv. Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving. arXiv preprint arXiv:2303.05760, 2023.
[28] Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. Human3.6M: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence, 36(7):1325-1339, 2014.
[29] Umar Iqbal, Anton Milan, and Juergen Gall. Posetrack: Joint multi-person pose estimation and tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2011–2020, 2017.
[30] Rohit Jena, Changliu Liu, and Katia Sycara. Augmenting gail with bc for sample efficient imitation learning. In Conference on Robot Learning, pages 80-90. PMLR, 2021.
[31] 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 Proceedings of the IEEE International Conference on Computer Vision, pages 3334-3342, 2015.
[32] Hanbyul Joo, Tomas Simon, Mina Cikara, and Yaser Sheikh. Towards social artificial intelligence: Nonverbal social signal prediction in a triadic interaction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10873-10883, 2019.
[33] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. *ICLR*, 2015.
[34] Vineet Kosaraju, Amir Sadeghian, Roberto Martin-Martin, Ian Reid, S Hamid Rezatofighi, and Silvio Savarese. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. arXiv preprint arXiv:1907.03395, 2019.
[35] Tao Li, Yuhan Zhao, and Quanyan Zhu. The role of information structures in game-theoretic multi-agent learning. Annual Reviews in Control, 2022.
[36] Xin Li, Xiaowen Ying, and Mooi Choo Chuah. Grip: Graph-based interaction-aware trajectory prediction. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pages 3960-3966. IEEE, 2019.
[37] Florian Loffing and Rouwen Cñal-Bruland. Anticipation in sport. Current opinion in psychology, 16:6–11, 2017.
[38] Yuanfu Luo, Panpan Cai, Aniket Bera, David Hsu, Wee Sun Lee, and Dinesh Manocha. Porca: Modeling and planning for autonomous driving among many pedestrians. IEEE Robotics and Automation Letters, 3(4):3418-3425, 2018.
[39] Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, and Adrien Gaidon. It is not the journey but the destination: Endpoint conditioned trajectory prediction. In European Conference on Computer Vision, pages 759-776. Springer, 2020.
[40] Wei Mao, Miaomiao Liu, and Mathieu Salzmann. History repeats itself: Human motion prediction via motion attention. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XIV 16, pages 474-489. Springer, 2020.
[41] Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Srinath Sridhar, Gerard Pons-Moll, and Christian Theobalt. Single-shot multi-person 3d pose estimation from monocular rgb. In 2018 International Conference on 3D Vision (3DV), pages 120-130. IEEE, 2018.
[42] Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14424-14432, 2020.
[43] Abduallah Mohamed, Deyao Zhu, Warren Vu, Mohamed Elhoseiny, and Christian Claudel. Social-implicit: Rethinking trajectory prediction evaluation and the effectiveness of implicit maximum likelihood estimation. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXII, pages 463–479. Springer, 2022.
[44] Mehdi Moussaïd, Niriaska Perozo, Simon Garnier, Dirk Helbing, and Guy Theraulaz. The walking behaviour of pedestrian social groups and its impact on crowd dynamics. *PloS one*, 5(4):e10047, 2010.
[45] Rosemarie Nagel. Unraveling in guessing games: An experimental study. The American economic review, 85(5):1313-1326, 1995.
[46] Andrew Y Ng, Stuart J Russell, et al. Algorithms for inverse reinforcement learning. In Icml, volume 1, page 2, 2000.
[47] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.