Animating Human Athletics
arXiv:2302.06108 · doi:10.1145/218380.218414
Abstract
This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondary motion to the animations with spring-mass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.
arXiv commentAlternate location: http://graphics.berkeley.edu/papers/Hodgins-AHA-1995-08 8 pages, figures
References in corpus (1)
Cited by in corpus (19)
- ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
- Automatic Joint Parameter Estimation from Magnetic Motion Capture Data
- Perception of Human Motion with Different Geometric Models
- Learning Symmetric and Low-energy Locomotion
- Animating Sand, Mud, and Snow
- Combining Active and Passive Simulations for Secondary Motion
- PADL: Language-Directed Physics-Based Character Control
- Motion In-Betweening with Phase Manifolds
- Generating Upper-Body Motion for Real-Time Characters Making their Way through Dynamic Environments
- MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
- Integration of a Balanced Virtual Manikin in a Virtual Reality Platform aimed at Virtual Prototyping
- SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
- Performance-Based Biped Control using a Consumer Depth Camera
- AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning
- Balanced Virtual Humans Interacting with their Environment
- Mathematics of Human Motion: from Animation towards Simulation (A View form the Outside)
- Discovering Diverse Athletic Jumping Strategies
- Computational Design of Skinned Quad-Robots
- Efficient Hyperparameter Optimization for Physics-based Character Animation