Abstract
Animated motions should be simple to direct while also being plausible. We present a flexible keyframe-based character animation system that generates plausible simulated motions for both physically-feasible and physically-infeasible motion specifications. We introduce a novel control parameterization, optimizing over internal actions, external assistive-force modulation, and keyframe timing. Our method allows for emergent behaviors between keyframes, does not require advance knowledge of contacts or exact motion timing, supports the creation of physically impossible motions, and allows for near-interactive motion creation. The use of a shooting method allows for the use of any black-box simulator. We present results for a variety of 2D and 3D characters and motions, using sparse and dense keyframes. We compare our control parameterization scheme against other possible approaches for incorporating external assistive forces.
Supplemental Material
Available for Download
Supplemental movie, appendix, image and software files for, Flexible Motion Optimization with Modulated Assistive Forces
- Shailen Agrawal, Shuo Shen, and Michiel van de Panne. 2013. Diverse motion variations for physics-based character animation. In Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation. 37--44.Google Scholar
- Mazen Al Borno, Martin De Lasa, and Aaron Hertzmann. 2012. Trajectory optimization for full-body movements with complex contacts. IEEE transactions on visualization and computer graphics 19, 8 (2012), 1405--1414.Google Scholar
- Mazen Al Borno, Michiel van de Panne, and Eugene Fiume. 2017. Domain of attraction expansion for physics-based character control. ACM Transactions on Graphics (TOG) 36, 2 (2017), 1--11.Google Scholar
Digital Library
- Yunfei Bai, Danny M Kaufman, C Karen Liu, and Jovan Popović. 2016. Artist-directed dynamics for 2D animation. ACM Transactions on Graphics (TOG) 35, 4 (2016), 1--10.Google Scholar
Digital Library
- Ronen Barzel, John R Hughes, and Daniel N Wood. 1996. Plausible motion simulation for computer graphics animation. In Computer Animation and Simulation'96. Springer, 183--197.Google Scholar
- Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes. 2019. DReCon: data-driven responsive control of physics-based characters. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--11.Google Scholar
Digital Library
- Danny Chapman, Tim Daoust, Andras Ormos, and Joseph Lewis. 2020. WeightShift: Accelerating Animation at Framestore with Physics. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, showcase proceedings. 1--2.Google Scholar
- Nuttapong Chentanez, Matthias Müller, Miles Macklin, Viktor Makoviychuk, and Stefan Jeschke. 2018. Physics-based motion capture imitation with deep reinforcement learning. In Proceedings of the 11th Annual International Conference on Motion, Interaction, and Games. 1--10.Google Scholar
Digital Library
- Erwin Coumans and Yunfei Bai. 2016--2020. PyBullet, a Python module for physics simulation for games, robotics and machine learning. http://pybullet.org.Google Scholar
- Danilo Borges da Silva, Rubens Fernandes Nunes, Creto Augusto Vidal, Joaquim B Cavalcante-Neto, Paul G Kry, and Victor B Zordan. 2017. Tunable robustness: An artificial contact strategy with virtual actuator control for balance. In Computer Graphics Forum, Vol. 36. Wiley Online Library, 499--510.Google Scholar
- Kai Ding, Libin Liu, Michiel Van de Panne, and KangKang Yin. 2015. Learning reduced-order feedback policies for motion skills. In Proceedings of the 14th ACM SIGGRAPH/Eurographics Symposium on Computer Animation. 83--92.Google Scholar
Digital Library
- Petros Faloutsos, Michiel Van de Panne, and Demetri Terzopoulos. 2001. Composable controllers for physics-based character animation. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques. 251--260.Google Scholar
Digital Library
- Sehoon Ha and C Karen Liu. 2014. Iterative training of dynamic skills inspired by human coaching techniques. ACM Transactions on Graphics (TOG) 34, 1 (2014), 1--11.Google Scholar
Digital Library
- Sehoon Ha, Yuting Ye, and C Karen Liu. 2012. Falling and landing motion control for character animation. ACM Transactions on Graphics (TOG) 31, 6 (2012), 1--9.Google Scholar
Digital Library
- Perttu Hämäläinen, Sebastian Eriksson, Esa Tanskanen, Ville Kyrki, and Jaakko Lehtinen. 2014. Online motion synthesis using sequential monte carlo. ACM Transactions on Graphics (TOG) 33, 4 (2014), 1--12.Google Scholar
Digital Library
- Daseong Han, Haegwang Eom, Junyong Noh, and Joseph S Shin. 2016. Data-guided model predictive control based on smoothed contact dynamics. In Computer Graphics Forum, Vol. 35. Wiley Online Library, 533--543.Google Scholar
- Nikolaus Hansen. 2016. The CMA evolution strategy: A tutorial. arXiv preprint arXiv:1604.00772 (2016).Google Scholar
- Nikolaus Hansen, Youhei Akimoto, yoshihikoueno, Dimo Brockhoff, and Matthew Chan. 2020. CMA-ES/pycma: r3.0.3. https://doi.org/10.5281/zenodo.3764210Google Scholar
- Eric Heiden, David Millard, Hejia Zhang, and Gaurav S Sukhatme. 2019. Interactive differentiable simulation. arXiv preprint arXiv:1905.10706 (2019).Google Scholar
- Sumit Jain, Yuting Ye, and C Karen Liu. 2009. Optimization-based interactive motion synthesis. ACM Transactions on Graphics (TOG) 28, 1 (2009), 1--12.Google Scholar
Digital Library
- Paul G Kry, Cyrus Rahgoshay, Amir Rabbani, and Karan Singh. 2012. Inverse kinodynamics: Editing and constraining kinematic approximations of dynamic motion. Computers & Graphics 36, 8 (2012), 904--915.Google Scholar
Digital Library
- Taesoo Kwon, Yoonsang Lee, and Michiel Van De Panne. 2020. Fast and flexible multilegged locomotion using learned centroidal dynamics. ACM Transactions on Graphics (TOG) 39, 4 (2020), 46--1.Google Scholar
Digital Library
- Sergey Levine and Jovan Popović. 2012. Physically plausible simulation for character animation. In Proceedings of the 11th ACM SIGGRAPH/Eurographics Symposium on Computer Animation. 221--230.Google Scholar
- C Karen Liu and Zoran Popović. 2002. Synthesis of complex dynamic character motion from simple animations. ACM Transactions on Graphics (TOG) 21, 3 (2002), 408--416.Google Scholar
Digital Library
- Libin Liu, KangKang Yin, Michiel van de Panne, Tianjia Shao, and Weiwei Xu. 2010. Sampling-based contact-rich motion control. In ACM SIGGRAPH 2010 papers. 1--10.Google Scholar
- Zicheng Liu, Steven J Gortler, and Michael F Cohen. 1994. Hierarchical spacetime control. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques. 35--42.Google Scholar
Digital Library
- Azumi Maekawa, Ryuma Niiyama, and Shunji Yamanaka. 2018. Pseudo-Locomotion Design with a Quadrotor-Assisted Biped Robot. In 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2462--2466.Google Scholar
Cross Ref
- Carlos Mastalli, Rohan Budhiraja, Wolfgang Merkt, Guilhem Saurel, Bilal Hammoud, Maximilien Naveau, Justin Carpentier, Ludovic Righetti, Sethu Vijayakumar, and Nicolas Mansard. 2020. Crocoddyl: An efficient and versatile framework for multi-contact optimal control. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2536--2542.Google Scholar
Cross Ref
- Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, and Nicolas Heess. 2020. Catch & Carry: reusable neural controllers for vision-guided whole-body tasks. ACM Transactions on Graphics (TOG) 39, 4 (2020), 39--1.Google Scholar
Digital Library
- Filipe Figueredo Monteiro, Andre Luiz Buarque Vieira, João Marcelo Xavier Natário Teixeira, Veronica Teichrieb, et al. 2019. Simulating real robots in virtual environments using NVIDIA's Isaac SDK. In Anais Estendidos do XXI Simpósio de Realidade Virtual e Aumentada. SBC, 47--48.Google Scholar
- Igor Mordatch, Emanuel Todorov, and Zoran Popović. 2012. Discovery of complex behaviors through contact-invariant optimization. ACM Transactions on Graphics (TOG) 31, 4 (2012), 1--8.Google Scholar
Digital Library
- Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee. 2019. Learning predict-and-simulate policies from unorganized human motion data. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--11.Google Scholar
Digital Library
- Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. 2018. Deepmimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1--14.Google Scholar
Digital Library
- Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel Van De Panne. 2017. Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Transactions on Graphics (TOG) 36, 4 (2017), 1--13.Google Scholar
Digital Library
- Michael Posa, Cecilia Cantu, and Russ Tedrake. 2014. A direct method for trajectory optimization of rigid bodies through contact. The International Journal of Robotics Research 33, 1 (2014), 69--81.Google Scholar
Digital Library
- Amir H Rabbani and Paul G Kry. 2016. PhysIK: Physically Plausible and Intuitive Keyframing.. In Graphics Interface. 153--161.Google Scholar
- Joose Rajamäki and Perttu Hämäläinen. 2018. Continuous control monte carlo tree search informed by multiple experts. IEEE transactions on visualization and computer graphics 25, 8 (2018), 2540--2553.Google Scholar
- Paul SA Reitsma and Nancy S Pollard. 2003. Perceptual metrics for character animation: sensitivity to errors in ballistic motion. In ACM SIGGRAPH 2003 Papers. 537--542.Google Scholar
Digital Library
- Kwang Won Sok, Katsu Yamane, Jehee Lee, and Jessica Hodgins. 2010. Editing dynamic human motions via momentum and force. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer animation. 11--20.Google Scholar
Digital Library
- Yuval Tassa, Tom Erez, and Emanuel Todorov. 2012. Synthesis and stabilization of complex behaviors through online trajectory optimization. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 4906--4913.Google Scholar
Cross Ref
- Michiel van de Panne and Alexis Lamouret. 1995. Guided optimization for balanced locomotion. In Computer Animation and Simulation'95. Springer, 165--177.Google Scholar
- Kevin Wampler and Zoran Popović. 2009. Optimal gait and form for animal locomotion. ACM Transactions on Graphics (TOG) 28, 3 (2009), 1--8.Google Scholar
Digital Library
- Andrew Witkin and Michael Kass. 1988. Spacetime constraints. ACM Siggraph Computer Graphics 22, 4 (1988), 159--168.Google Scholar
Digital Library
- Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020. A scalable approach to control diverse behaviors for physically simulated characters. ACM Transactions on Graphics (TOG) 39, 4 (2020), 33--1.Google Scholar
Digital Library
- Pawel Wrotek, Odest Chadwicke Jenkins, and Morgan McGuire. 2006. Dynamo: dynamic, data-driven character control with adjustable balance. In Proceedings of the 2006 ACM SIGGRAPH symposium on Videogames. 61--70.Google Scholar
Digital Library
- KangKang Yin, Stelian Coros, Philippe Beaudoin, and Michiel Van de Panne. 2008. Continuation methods for adapting simulated skills. In ACM SIGGRAPH 2008 papers. 1--7.Google Scholar
Digital Library
- Wenhao Yu, Greg Turk, and C Karen Liu. 2018. Learning symmetric and low-energy locomotion. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1--12.Google Scholar
Digital Library
- Victor Zordan, David Brown, Adriano Macchietto, and KangKang Yin. 2014. Control of rotational dynamics for ground and aerial behavior. IEEE transactions on visualization and computer graphics 20, 10 (2014), 1356--1366.Google Scholar
Cross Ref
- Victor Brian Zordan and Jessica K. Hodgins. 2002. Motion Capture-Driven Simulations That Hit and React. In Proceedings of the 2002 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (San Antonio, Texas) (SCA '02). Association for Computing Machinery, New York, NY, USA, 89--96. https://doi.org/10.1145/545261.545276Google Scholar
Index Terms
Flexible Motion Optimization with Modulated Assistive Forces
Recommendations
Extended spatial keyframing for complex character animation
CASA'2008 Special IssueAs 3D computer animation becomes more accessible to novice users, it makes it possible for these users to create high-quality animations. This paper introduces a more powerful system to create highly articulated character animations with an intuitive ...
Advanced use cases for animation rigging in unity
SIGGRAPH '19: ACM SIGGRAPH 2019 StudioThe Animation Rigging package for Unity enables users to setup rigs to procedurally control skeletal animations as a post-process. Attendees of this Studio Workshop will get hands-on experience working with this system in step-by-step tutorials. We will ...
DeepMimic: example-guided deep reinforcement learning of physics-based character skills
A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We ...






Comments