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Learning to predict agent motions with relationship reasoning is important for many applications.
Social force model for pedestrian dynamics
Dirk Helbing and Peter Molnar · 1995
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Crowds by example
Alon Lerner, Yiorgos Chrysanthou, and Dani Lischinski · 2007
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Gaussian process dynamical models for human motion
Jack M Wang, David J Fleet, and Aaron Hertzmann · 2007
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Abnormal crowd behavior detection using social force model
Ramin Mehran, Alexis Oyama, and Mubarak Shah · 2009
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You’ll never walk alone: Modeling social behavior for multi-target tracking
Stefano Pellegrini, Andreas Ess, Konrad Schindler, and Luc Van Gool · 2009
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Factored conditional restricted boltzmann machines for modeling motion style
Graham W Taylor and Geoffrey E Hinton · 2009
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Towards fully autonomous driving: Systems and algorithms
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Activity forecasting
Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert · 2012
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Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2013
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Efficient nonlinear markov models for human motion
Andreas M Lehrmann, Peter V Gehler, and Sebastian Nowozin · 2014
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Recurrent network models for human dynamics
Katerina Fragkiadaki, Sergey Levine, Panna Felsen, and Jitendra Malik · 2015
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Social lstm: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Structural-rnn: Deep learning on spatio-temporal graphs
Ashesh Jain, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena · 2016
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Analysis of recurrent neural networks for probabilistic modeling of driver behavior
Jeremy Morton, Tim A Wheeler, and Mykel J Kochenderfer · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 2017
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Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, and Kostas Daniilidis · 2017
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Desire: Distant future prediction in dynamic scenes with interacting agents
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker · 2017
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Rotation equivariant vector field networks
Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia · 2017
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On human motion prediction using recurrent neural networks
Julieta Martinez, Michael J Black, and Javier Romero · 2017
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The pose knows: Video forecasting by generating pose futures
Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert · 2017
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Harmonic networks: Deep translation and rotation equivariance
Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow · 2017
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Intentnet: Learning to predict intention from raw sensor data
Sergio Casas, Wenjie Luo, and Raquel Urtasun · 2018
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Social gan: Socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
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Neural relational inference for interacting systems
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Convolutional sequence to sequence model for human dynamics
Chen Li, Zhen Zhang, Wee Sun Lee, and Gim Hee Lee · 2018
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Flexible neural representation for physics prediction
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Social attention: Modeling attention in human crowds
Anirudh Vemula, Katharina Muelling, and Jean Oh · 2018
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Learning steerable filters for rotation equivariant cnns
Maurice Weiler, Fred A Hamprecht, and Martin Storath · 2018
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Trajectron++: Multi-agent generative trajectory forecasting with heterogeneous data for control
Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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Factorizable graph convolutional networks
Yiding Yang, Zunlei Feng, Mingli Song, and Xinchao Wang · 2020
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Distilling knowledge from graph convolutional networks
Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang · 2020
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Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction
Lingwei Dang, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li · 2021
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Vector neurons: A general framework for so (3)-equivariant networks
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Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov · 2019
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Human motion prediction via learning local structure representations and temporal dependencies
Xiao Guo and Jongmoo Choi · 2019
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Equivariant flows: sampling configurations for multi-body systems with symmetric energies
Jonas Köhler, Leon Klein, and Frank Noé · 2019
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Actional-structural graph convolutional networks for skeleton-based action recognition
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, and Qi Tian · 2019
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Learning trajectory dependencies for human motion prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann, and Hongdong Li · 2019
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Hamiltonian graph networks with ode integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia · 2019
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Lagrangian fluid simulation with continuous convolutions
Benjamin Ummenhofer, Lukas Prantl, Nils Thuerey, and Vladlen Koltun · 2019
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Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J Guibas · 2021
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Transformer networks for trajectory forecasting
Francesco Giuliari, Irtiza Hasan, Marco Cristani, and Fabio Galasso · 2021
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Lietransformer: Equivariant self-attention for lie groups
Michael J Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, and Hyunjik Kim · 2021
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Roto-translated local coordinate frames for interacting dynamical systems
Miltiadis Kofinas, Naveen Nagaraja, and Efstratios Gavves · 2021
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Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction
Maosen Li, Siheng Chen, Xu Chen, Ya Zhang, Yanfeng Wang, and Qi Tian · 2021
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E (n) equivariant graph neural networks
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Collaborative uncertainty in multi-agent trajectory forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya Zhang, and Siheng Chen · 2021
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Invariant teacher and equivariant student for unsupervised 3d human pose estimation
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Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting
Ye Yuan, Xinshuo Weng, Yanglan Ou, and Kris M Kitani · 2021
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Learning pedestrian group representations for multi-modal trajectory prediction
Inhwan Bae, Jin-Hwi Park, and Hae-Gon Jeon · 2022
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Stochastic trajectory prediction via motion indeterminacy diffusion
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Equivariant graph mechanics networks with constraints
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Skeleton-parted graph scattering networks for 3d human motion prediction
Maosen Li, Siheng Chen, Zijing Zhang, Lingxi Xie, Qi Tian, and Ya Zhang · 2022
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Progressively generating better initial guesses towards next stages for high-quality human motion prediction
Tiezheng Ma, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li · 2022
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Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning
Chenxin Xu, Maosen Li, Zhenyang Ni, Ya Zhang, and Siheng Chen · 2022
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Remember intentions: Retrospective-memory-based trajectory prediction
Chenxin Xu, Weibo Mao, Wenjun Zhang, and Siheng Chen · 2022
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Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning
Chenxin Xu, Yuxi Wei, Bohan Tang, Sheng Yin, Ya Zhang, and Siheng Chen · 2022
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Factorizing knowledge in neural networks
Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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Deep model reassembly
Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, and Xinchao Wang · 2022
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Aware of the history: Trajectory forecasting with the local behavior data
Yiqi Zhong, Zhenyang Ni, Siheng Chen, and Ulrich Neumann · 2022
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