C. D. Tharindu Mathew
Bedrich Benes
ABSTRACT
We introduce a new inverse modeling method to interactively design crowd animations. Few works focus on providing succinct high-level and large-scale crowd motion modeling. Our methodology is to read in real or virtual agent trajectory data and automatically infer a set of parameterized crowd motion models. Then, components of the motion models can be mixed, matched, and altered enabling rapidly producing new crowd motions. Our results show novel animations using real-world data, using synthetic data, and imitating real-world scenarios. Moreover, by combining our method with our interactive crowd trajectory sketching tool, we can create complex spatio-temporal crowd animations in about a minute.
- Kfir Aberman, Rundi Wu, Dani Lischinski, Baoquan Chen, and Daniel Cohen-Or. 2019. Learning Character-agnostic Motion for Motion Retargeting in 2D. ACM Trans. Graph. 38, 4, Article 75 (2019), 75:1–75:14 pages.Google Scholar
Digital Library
- Richard Bellman. 2013. Dynamic programming. Courier Corporation.Google ScholarDigital Library
- Aniket Bera, Sujeong Kim, and Dinesh Manocha. 2015. Efficient Trajectory Extraction and Parameter Learning for Data-driven Crowd Simulation. In Proc. of the 41st Graphics Interface Conference(GI ’15). 65–72.Google ScholarDigital Library
- Martin Bokeloh, Michael Wand, and Hans-Peter Seidel. 2010. A connection between partial symmetry and inverse procedural modeling. In ACM Trans. Graph, Vol. 29. ACM, 104.Google Scholar
- Alessandro de Lima Bicho, Rafael Araújo Rodrigues, Soraia Raupp Musse, Cláudio Rosito Jung, Marcelo Paravisi, and Léo Pini Magalhães. 2012. Simulating crowds based on a space colonization algorithm. Comp. & Graph. 36, 2 (2012), 70–79.Google ScholarDigital Library
- Ilke Demir, Daniel G Aliaga, and Bedrich Benes. 2016. Proceduralization for editing 3d architectural models. In 3D Vision (3DV). IEEE, 194–202.Google Scholar
- T. B. Dutra, R. Marques, J.B. Cavalcante-Neto, C. A. Vidal, and J. Pettré. 2017. Gradient-based Steering for Vision-based Crowd Simulation Algorithms. Comput. Graph. Forum 36, 2 (May 2017), 337–348. http://dl.acm.org/citation.cfm?id=3128975.3129006Google Scholar
- Arnaud Emilien, Ulysse Vimont, Marie-Paule Cani, Pierre Poulin, and Bedrich Benes. 2015. WorldBrush: Interactive Example-based Synthesis of Procedural Virtual Worlds. ACM Trans. Graph. 34, 4, Article 106 (July 2015), 11 pages.Google ScholarDigital Library
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, 1996. A density-based algorithm for discovering clusters in large spatial databases with noise.. In Kdd, Vol. 96. 226–231.Google Scholar
- Ignacio Garcia-Dorado, Daniel G. Aliaga, and Satish V. Ukkusuri. 2014. Designing large-scale interactive traffic animations for urban modeling. Comp. Graph. Forum 33, 2 (2014), 411–420.Google ScholarDigital Library
- Abhinav Golas, Rahul Narain, and Ming Lin. 2013. Hybrid Long-range Collision Avoidance for Crowd Simulation. In Proc. of I3D(I3D ’13). ACM, 29–36.Google ScholarDigital Library
- Kevin Jordao, Panayiotis Charalambous, Marc Christie, Julien Pettré, and Marie-Paule Cani. 2015. Crowd art: density and flow based crowd motion design. In MIG.Google Scholar
- Kevin Jordao, Julien Pettré, Marc Christie, and Marie-Paule Cani. 2014. Crowd sculpting: A space-time sculpting method for populating virtual environments. Comput. Graph. Forum 33(2014), 351–360.Google ScholarDigital Library
- Eunjung Ju, Myung Geol Choi, Minji Park, Jehee Lee, Kang Hoon Lee, and Shigeo Takahashi. 2010. Morphable Crowds. In ACM SIGGRAPH Asia. ACM, Article 140, 10 pages. https://doi.org/10.1145/1866158.1866162Google ScholarDigital Library
- Alain Juarez-Perez and Marcelo Kallmann. 2018. Fast Behavioral Locomotion with Layered Navigation Meshes. In Proc. of I3D(I3D ’18). ACM, Article 8, 6 pages. https://doi.org/10.1145/3190834.3190841Google ScholarDigital Library
- Mubbasir Kapadia, Shawn Singh, Brian Allen, Glenn Reinman, and Petros Faloutsos. 2009. SteerBug: An Interactive Framework for Specifying and Detecting Steering Behaviors. In Proc. of SCA(SCA ’09). ACM, 209–216.Google ScholarDigital Library
- Mubbasir Kapadia, Xu Xianghao, Maurizio Nitti, Marcelo Kallmann, Stelian Coros, Robert W. Sumner, and Markus Gross. 2016. Precision: Precomputing Environment Semantics for Contact-rich Character Animation. In Proc. of I3D(I3D ’16). 29–37.Google ScholarDigital Library
- Ioannis Karamouzas, Brian Skinner, and Stephen J Guy. 2014. Universal power law governing pedestrian interactions. Physical Rev. letters 113, 23 (2014), 238701.Google Scholar
- Ioannis Karamouzas, Nick Sohre, Ran Hu, and Stephen J. Guy. 2018. Crowd Space: A Predictive Crowd Analysis Technique. ACM Trans. Graph. 37, 6, Article 186 (Dec. 2018), 14 pages. https://doi.org/10.1145/3272127.3275079Google ScholarDigital Library
- Ioannis Karamouzas, Nick Sohre, Rahul Narain, and Stephen J. Guy. 2017. Implicit Crowds: Optimization Integrator for Robust Crowd Simulation. ACM Trans. Graph. 36, 4, Article 136 (2017), 13 pages. https://doi.org/10.1145/3072959.3073705Google ScholarDigital Library
- Jongmin Kim, Yeongho Seol, Taesoo Kwon, and Jehee Lee. 2014. Interactive Manipulation of Large-scale Crowd Animation. ACM Trans. Graph. 33, 4, Article 83 (July 2014), 10 pages.Google ScholarDigital Library
- Sujeong Kim, Stephen J. Guy, Dinesh Manocha, and Ming C. Lin. 2012. Interactive Simulation of Dynamic Crowd Behaviors Using General Adaptation Syndrome Theory. In Proc. of I3D(I3D ’12). ACM, 55–62.Google ScholarDigital Library
- Eugene L Lawler. 1972. A procedure for computing the k best solutions to discrete optimization problems and its application to the shortest path problem. Management science 18, 7 (1972), 401–405.Google Scholar
- Kang Hoon Lee, Myung Choi, Qyoun Hong, and J. Lee. 2007. Group behavior from video: A data-driven approach to crowd simulation. Proc. of SCA 2007, 109–118. https://doi.org/10.1145/1272690.1272706Google Scholar
- Alon Lerner, Yiorgos Chrysanthou, and Dani Lischinski. 2007. Crowds by Example. Comput. Graph. Forum 26(2007), 655–664.Google ScholarCross Ref
- Yi Li, Marc Christie, Orianne Siret, Richard Kulpa, and Julien Pettré. 2012. Cloning Crowd Motions. In Proc. of SCA(SCA ’12). Eurographics Association, 201–210.Google Scholar
- Rahul Narain, Abhinav Golas, Sean Curtis, and Ming C. Lin. 2009. Aggregate Dynamics for Dense Crowd Simulation. ACM Trans. Graph. 28, 5, Article 122 (2009), 122:1–122:8 pages.Google ScholarDigital Library
- Gen Nishida, Adrien Bousseau, and Daniel G Aliaga. 2018. Procedural Modeling of a Building from a Single Image. In Comp. Graph. Forum, Vol. 37. 415–429.Google Scholar
- Gen Nishida, Ignacio Garcia-Dorado, Daniel G. Aliaga, Bedrich Benes, and Adrien Bousseau. 2016. Interactive Sketching of Urban Procedural Models. ACM Trans. Graph. 35, 4, Article 130 (July 2016), 11 pages.Google ScholarDigital Library
- Aline Normoyle, Maxim Likhachev, and Alla Safonova. 2014. Stochastic Activity Authoring with Direct User Control. In Proc. of I3D(I3D ’14). ACM, 31–38.Google ScholarDigital Library
- Chonhyon Park, Jae Sung Park, Steve Tonneau, Nicolas Mansard, Franck Multon, Julien Pettré, and Dinesh Manocha. 2016. Dynamically Balanced and Plausible Trajectory Planning for Human-like Characters. In Proc. of I3D(I3D ’16). 39–48.Google ScholarDigital Library
- Sachin Patil, Jur van den Berg, Sean Curtis, Ming C. Lin, and Dinesh Manocha. 2011. Directing Crowd Simulations Using Navigation Fields. IEEE Transactions on Visualization and Computer Graphics 17, 2 (Feb. 2011), 244–254.Google ScholarDigital Library
- Mikel Rodriguez, Ivan Laptev, Josef Sivic, and Jean-Yves Audibert. 2011. Density-aware person detection and tracking in crowds. 2011 International Conference on Computer Vision (2011), 2423–2430.Google ScholarDigital Library
- Arno Schödl, Richard Szeliski, David H. Salesin, and Irfan Essa. 2000. Video Textures. In Proc. of Siggraph. 489–498.Google Scholar
- Boltes Maik Seyfried, Armin. 2019. Pedestrian Dynamics Data Archive. website: http://ped.fz-juelich.de/da/doku.php?id=start; accessed January 10, 2019.Google Scholar
- Alexander Shoulson, Nathan Marshak, Mubbasir Kapadia, and Norman I. Badler. 2013. ADAPT: The Agent Development and Prototyping Testbed. In Proc. of I3D(I3D ’13). 9–18.Google ScholarDigital Library
- Shawn Singh, Mubbasir Kapadia, Billy Hewlett, Glenn Reinman, and Petros Faloutsos. 2011. A Modular Framework for Adaptive Agent-based Steering. In I3D(I3D ’11). ACM, 141–150 PAGE@9.Google Scholar
- Ondrej Stava, B. Benes, Radomír Měch, D. G. Aliaga, and P. Kristof. 2010. Inverse procedural modeling by automatic generation of L-systems. 29, 2 (2010), 665–674.Google Scholar
- Ondrej Stava, Sören Pirk, Julian Kratt, Baoquan Chen, Radomir Mech, Oliver Deussen, and Bedrich Benes. 2014a. Inverse Procedural Modelling of Trees. Comp. Graph. Forum 33, 6 (2014), 118–131.Google ScholarDigital Library
- Ondrej. Stava, Sören. Pirk, Julain Kratt, Baoquan Chen, Radomir Měch, Oliver Deussen, and Benes Benes. 2014b. Inverse Procedural Modelling of Trees. Comp. Graph. Forum 33, 6 (2014), 118–131. https://doi.org/10.1111/cgf.12282 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.12282Google ScholarDigital Library
- Mankyu Sung, Michael Gleicher, and Stephen Chenney. 2004. Scalable behaviors for crowd simulation. Comp. Graph. Forum (2004).Google Scholar
- Shigeo Takahashi, Kenichi Yoshida, Taesoo Kwon, Kang Hoon Lee, Jehee Lee, and Sung Yong Shin. 2009. Spectral-Based Group Formation Control. Comput. Graph. Forum 28(2009), 639–648.Google ScholarCross Ref
- Jerry Talton, Lingfeng Yang, Ranjitha Kumar, Maxine Lim, Noah Goodman, and Radomír Měch. 2012. Learning design patterns with bayesian grammar induction. In UIST. ACM, 63–74.Google Scholar
- Jerry O Talton, Yu Lou, Steve Lesser, Jared Duke, Radomír Měch, and Vladlen Koltun. 2011. Metropolis proced. modeling. ACM Trans. Graph. 30, 2 (2011), 11.Google ScholarDigital Library
- Adrien Treuille, Seth Cooper, and Zoran Popović. 2006. Continuum crowds. ACM Trans. Graph. 25, 3 (2006), 1160–1168.Google ScholarDigital Library
- Branislav Ulicny, Pablo de Heras Ciechomski, and Daniel Thalmann. 2004. Crowdbrush: Interactive Authoring of Real-time Crowd Scenes. In Proc. of SCA(SCA ’04). Eurographics Association, 243–252.Google Scholar
- Jur van den Berg, Stephen J. Guy, Ming Lin, and Dinesh Manocha. 2011. Reciprocal n-Body Collision Avoidance. In Robotics Research, Cédric Pradalier, Roland Siegwart, and Gerhard Hirzinger(Eds.). Springer Berlin Heidelberg, 3–19.Google Scholar
- Carlos A. Vanegas, Daniel G. Aliaga, and Bedrich Benes. 2010. Building reconstruction using manhattan-world grammars. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on 0 (2010), 358–365.Google Scholar
- Carlos A. Vanegas, Ignacio Garcia-Dorado, Daniel G. Aliaga, Bedrich Benes, and Paul Waddell. 2012. Inverse Design of Urban Procedural Models. ACM Trans. Graph. 31, 6, Article 168 (Nov. 2012), 11 pages.Google ScholarDigital Library
- He Wang and Carol O Sullivan. 2016. Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos. In Computer Vision – ECCV 2016, Vol. 9909.Google ScholarCross Ref
- He Wang, Jan Ondřej, and Carol O Sullivan. 2016. Path Patterns: Analyzing and Comparing Real and Simulated Crowds. In Proc. of I3D(I3D ’16). ACM, 49–57.Google ScholarDigital Library
- D. Wolinski, S. J. Guy, A.-H. Olivier, M. Lin, D. Manocha, and J. Pettré. 2014. Parameter Estimation and Comparative Evaluation of Crowd Simulations. Comput. Graph. Forum 33, 2 (May 2014), 303–312.Google Scholar
- David Wolinski, Ming C. Lin, and Julien Pettré. 2016. WarpDriver: Context-aware Probabilistic Motion Prediction for Crowd Simulation. ACM Trans. Graph. 35, 6, Article 164 (Nov. 2016), 11 pages.Google ScholarDigital Library
- Sai-Keung Wong, Yi-Hung Chou, and Hsiang-Yu Yang. 2018. A Framework for Simulating Agent-based Cooperative Tasks in Crowd Simulation. In Proc. of I3D(I3D ’18). ACM, Article 11, 10 pages. https://doi.org/10.1145/3190834.3190839Google ScholarDigital Library
- Barbara Yersin, Jonathan Maïm, Julien Pettré, and Daniel Thalmann. 2009. Crowd Patches: Populating Large-scale Virtual Environments for Real-time Applications. In Proc. of I3D(I3D ’09). ACM, 207–214.Google ScholarDigital Library
Recommendations
Crowd motion capture
CASA 2007In this paper a new and original technique to animate a crowd of human beings is presented. Following the success of data-driven animation models (such as motion capture) in the context of articulated figures control, we propose to derivate a similar ...
Group motion editing
Animating a crowd of characters is an important problem in computer graphics. The latest techniques enable highly realistic group motions to be produced in feature animation films and video games. However, interactive methods have not emerged yet for ...
Inverse design of urban procedural models
We propose a framework that enables adding intuitive high level control to an existing urban procedural model. In particular, we provide a mechanism to interactively edit urban models, a task which is important to stakeholders in gaming, urban planning, ...
Comments