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Modeling and reproducing crowd behaviors are important in various domains including psychology, robotics, transport engineering and virtual environments.
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Motion patches: building blocks for virtual environments annotated with motion data
Kang Hoon Lee, Myung Geol Choi, and Jehee Lee · 2006
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Group behavior from video: a data-driven approach to crowd simulation
Kang Hoon Lee, Myung Geol Choi, Qyoun Hong, and Jehee Lee · 2007
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Crowds by example
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Toward accurate dynamic time warping in linear time and space
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Julien Pettré · 2008
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Reciprocal velocity obstacles for real-time multi-agent navigation
Jur Van den Berg, Ming Lin, and Dinesh Manocha · 2008
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How the ocean personality model affects the perception of crowds
Funda Durupinar, Nuria Pelechano, Jan Allbeck, Uǧur Güdükbay, and Norman I Badler · 2009
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Statistical models of pedestrian behaviour in the forum
Barbara Majecka · 2009
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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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Crowd patches: populating large-scale virtual environments for real-time applications
Barbara Yersin, Jonathan Maïm, Julien Pettré, and Daniel Thalmann · 2009
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Morphable crowds
Eunjung Ju, Myung Geol Choi, Minji Park, Jehee Lee, Kang Hoon Lee, and Shigeo Takahashi · 2010
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Context-dependent crowd evaluation
Alon Lerner, Yiorgos Chrysanthou, Ariel Shamir, and Daniel Cohen-Or · 2010
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The walking behaviour of pedestrian social groups and its impact on crowd dynamics
Mehdi Moussaïd, Niriaska Perozo, Simon Garnier, Dirk Helbing, and Guy Theraulaz · 2010
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Optimal reciprocal collision avoidance for multi-agent navigation
Jur Van Den Berg, Stephen J Guy, Jamie Snape, Ming Lin, and Dinesh Manocha · 2010
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Simulating heterogeneous crowd behaviors using personality trait theory
Stephen J Guy, Sujeong Kim, Ming C Lin, and Dinesh Manocha · 2011
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Who are you with and where are you going?
Kota Yamaguchi, Alexander C Berg, Luis E Ortiz, and Tamara L Berg · 2011
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A statistical similarity measure for aggregate crowd dynamics
Stephen J Guy, Jur Van Den Berg, Wenxi Liu, Rynson Lau, Ming C Lin, and Dinesh Manocha · 2012
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Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory
Sujeong Kim, Stephen J Guy, Dinesh Manocha, and Ming C Lin · 2012
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Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents
Bolei Zhou, Xiaogang Wang, and Xiaoou Tang · 2012
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Recast Navigation
Mononen, Mikko · 2014
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Goal-directed pedestrian prediction
Eike Rehder and Horst Kloeden · 2015
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Understanding pedestrian behaviors from stationary crowd groups
Shuai Yi, Hongsheng Li, and Xiaogang Wang · 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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Recurrent switching linear dynamical systems
Scott W Linderman, Andrew C Miller, Ryan P Adams, David M Blei, Liam Paninski, and Matthew J Johnson · 2016
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Learning social etiquette: Human trajectory understanding in crowded scenes
Alexandre Robicquet, Amir Sadeghian, Alexandre Alahi, and Silvio Savarese · 2016
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A comparative study of navigation meshes
Wouter Van Toll, Roy Triesscheijn, Marcelo Kallmann, Ramon Oliva, Nuria Pelechano, Julien Pettré, and Roland Geraerts · 2016
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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A disentangled recognition and nonlinear dynamics model for unsupervised learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 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 H. S. Torr, and Manmohan Chandraker · 2017
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Group modeling: A unified velocity-based approach
Zhiguo Ren, Panayiotis Charalambous, Julien Bruneau, Qunsheng Peng, and Julien Pettré · 2017
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Human trajectory prediction using spatially aware deep attention models
Daksh Varshneya and G. Srinivasaraghavan · 2017
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Group lstm: Group trajectory prediction in crowded scenarios
Niccoló Bisagno, Bo Zhang, and Nicola Conci · 2018
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Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection
Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes · 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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Crowd space: a predictive crowd analysis technique
Ioannis Karamouzas, Nick Sohre, Ran Hu, and Stephen J Guy · 2018
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Crowd simulation by deep reinforcement learning
Jaedong Lee, Jungdam Won, and Jehee Lee · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Scene-lstm: A model for human trajectory prediction
Huynh Manh and Gita Alaghband · 2018
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A data-driven model for interaction-aware pedestrian motion prediction in object cluttered environments
Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer, Juan I. Nieto, Roland Y. Siegwart, and César Cadena · 2018
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Pedestrian prediction by planning using deep neural networks
Eike Rehder, Florian Wirth, Martin Lauer, and Christoph Stiller · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Social attention: Modeling attention in human crowds
Anirudh Vemula, Katharina Muelling, and Jean Oh · 2018
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Ss-lstm: A hierarchical lstm model for pedestrian trajectory prediction
Hao Xue, Du Q Huynh, and Mark Reynolds · 2018
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Clust: simulating realistic crowd behaviour by mining pattern from crowd videos
Mingbi Zhao, Wentong Cai, and Stephen John Turner · 2018
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Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction
Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov · 2019
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Stgat: Modeling spatial-temporal interactions for human trajectory prediction
Yingfan Huang, Huikun Bi, Zhaoxin Li, Tianlu Mao, and Zhaoqi Wang · 2019
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The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs
A2x: An end-to-end framework for assessing agent and environment interactions in multimodal human trajectory prediction
Samuel S Sohn, Mihee Lee, Seonghyeon Moon, Gang Qiao, Muhammad Usman, Sejong Yoon, Vladimir Pavlovic, and Mubbasir Kapadia · 2022
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Human trajectory prediction with momentary observation
Jianhua Sun, Yuxuan Li, Liang Chai, Hao-Shu Fang, Yong-Lu Li, and Cewu Lu · 2022
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Social-ssl: Self-supervised cross-sequence representation learning based on transformers for multi-agent trajectory prediction
Li-Wu Tsao, Yan-Kai Wang, Hao-Siang Lin, Hong-Han Shuai, Lai-Kuan Wong, and Wen-Huang Cheng · 2022
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Stepwise goal-driven networks for trajectory prediction
Chuhua Wang, Yuchen Wang, Mingze Xu, and David J Crandall · 2022
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Social ode: Multi-agent trajectory forecasting with neural ordinary differential equations
Song Wen, Hao Wang, and Dimitris Metaxas · 2022
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Boris Ivanovic and Marco Pavone · 2019
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Meta-sim: Learning to generate synthetic datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, and Sanja Fidler · 2019
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Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, and Silvio Savarese · 2019
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Conditional generative neural system for probabilistic trajectory prediction
Jiachen Li, Hengbo Ma, and Masayoshi Tomizuka · 2019
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Which way are you going? imitative decision learning for path forecasting in dynamic scenes
Yuke Li · 2019
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Peeking into the future: Predicting future person activities and locations in videos
Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander G Hauptmann, and Li Fei-Fei · 2019
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Assessing the perceived realism of agent grouping dynamics for adaptation and simulation
Stuart O’Connor, James Shuttleworth, Simon Colreavy-Donnelly, and Fotis Liarokapis · 2019
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View vertically: A hierarchical network for trajectory prediction via fourier spectrums
Conghao Wong, Beihao Xia, Ziming Hong, Qinmu Peng, Wei Yuan, Qiong Cao, Yibo Yang, and Xinge You · 2022
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Cscnet: Contextual semantic consistency network for trajectory prediction in crowded spaces
Beihao Xia, Conghao Wong, Qinmu Peng, Wei Yuan, and Xinge You · 2022
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Human trajectory prediction via neural social physics
Jiangbei Yue, Dinesh Manocha, and He Wang · 2022
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Social-aware pedestrian trajectory prediction via states refinement lstm
Pu Zhang, Jianru Xue, Pengfei Zhang, Nanning Zheng, and Wanli Ouyang · 2022
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Adapt: Efficient multi-agent trajectory prediction with adaptation
Görkay Aydemir, Adil Kaan Akan, and Fatma Güney · 2023
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A set of control points conditioned pedestrian trajectory prediction
Inhwan Bae and Hae-Gon Jeon · 2023
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EigenTrajectory: Low-rank descriptors for multi-modal trajectory forecasting
Inhwan Bae, Jean Oh, and Hae-Gon Jeon · 2023
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Greil-crowds: crowd simulation with deep reinforcement learning and examples
Panayiotis Charalambous, Julien Pettre, Vassilis Vassiliades, Yiorgos Chrysanthou, and Nuria Pelechano · 2023
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R-pred: Two-stage motion prediction via tube-query attention-based trajectory refinement
Sehwan Choi, Jungho Kim, Junyong Yun, and Jun Won Choi · 2023
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Sparse instance conditioned multimodal trajectory prediction
Yonghao Dong, Le Wang, Sanping Zhou, and Gang Hua · 2023
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Trafficgen: Learning to generate diverse and realistic traffic scenarios
Lan Feng, Quanyi Li, Zhenghao Peng, Shuhan Tan, and Bolei Zhou · 2023
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Motiondiffuser: Controllable multi-agent motion prediction using diffusion
Chiyu Jiang, Andre Cornman, Cheolho Park, Benjamin Sapp, Yin Zhou, Dragomir Anguelov, et al · 2023
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Semi-supervised semantics-guided adversarial training for robust trajectory prediction
Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, and Qi Zhu · 2023
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Graph switching dynamical systems
Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, and Efstratios Gavves · 2023
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Fast inference and update of probabilistic density estimation on trajectory prediction
Takahiro Maeda and Norimichi Ukita · 2023
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Leapfrog diffusion model for stochastic trajectory prediction
Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen, and Yanfeng Wang · 2023
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Learn tarot with mentor: A meta-learned self-supervised approach for trajectory prediction
Mozhgan Pourkeshavarz, Changhe Chen, and Amir Rasouli · 2023
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Trace and pace: Controllable pedestrian animation via guided trajectory diffusion
Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, and Or Litany · 2023
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Motionlm: Multi-agent motion forecasting as language modeling
Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S Refaat, Rami Al-Rfou, and Benjamin Sapp · 2023
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Stimulus verification is a universal and effective sampler in multi-modal human trajectory prediction
Jianhua Sun, Yuxuan Li, Liang Chai, and Cewu Lu · 2023
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Language conditioned traffic generation
Shuhan Tan, Boris Ivanovic, Xinshuo Weng, Marco Pavone, and Philipp Kraehenbuehl · 2023
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Fend: A future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction
Yuning Wang, Pu Zhang, Lei Bai, and Jianru Xue · 2023
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Int2: Interactive trajectory prediction at intersections
Zhijie Yan, Pengfei Li, Zheng Fu, Shaocong Xu, Yongliang Shi, Xiaoxue Chen, Yuhang Zheng, Yang Li, Tianyu Liu, Chuxuan Li, et al · 2023
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Trajpac: Towards robustness verification of pedestrian trajectory prediction models
Liang Zhang, Nathaniel Xu, Pengfei Yang, Gaojie Jin, Cheng-Chao Huang, and Lijun Zhang · 2023
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Identifying nonstationary causal structures with high-order markov switching models
Carles Balsells-Rodas, Yixin Wang, Pedro AM Mediano, and Yingzhen Li · 2024
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Social physics informed diffusion model for crowd simulation
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Distilling knowledge for short-to-long term trajectory prediction
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