# Animating Human Athletics

arXiv: https://arxiv.org/abs/2302.06108 · HTML: https://mlsanity.com/paper/2302.06108/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2023.

## Built on by (most cited first, 17 of 17)

- [PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction](https://mlsanity.com/paper/2312.04393/) (2023)
- [Neural Categorical Priors for Physics-Based Character Control](https://mlsanity.com/paper/2308.07200/) (2023)
- [SkillMimic: Learning Basketball Interaction Skills from Demonstrations](https://mlsanity.com/paper/2408.15270/) (2024)
- [Motion In-Betweening with Phase Manifolds](https://mlsanity.com/paper/2308.12751/) (2023)
- [Strategy and Skill Learning for Physics-based Table Tennis Animation](https://mlsanity.com/paper/2407.16210/) (2024)
- [SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation](https://mlsanity.com/paper/2407.10481/) (2024)
- [SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control](https://mlsanity.com/paper/2512.03028/) (2025)
- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [Physics-Based Motion Imitation with Adversarial Differential Discriminators](https://mlsanity.com/paper/2505.04961/) (2025)
- [AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2505.23708/) (2025)
- [InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs](https://mlsanity.com/paper/2512.07410/) (2025)
- [Controlling Character Motions without Observable Driving Source](https://mlsanity.com/paper/2308.06025/) (2023)
- [MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters](https://mlsanity.com/paper/2311.02502/) (2023)
- [Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions](https://mlsanity.com/paper/2512.08500/) (2025)
- [ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting](https://mlsanity.com/paper/2605.06593/) (2026)
- [GPC: Large-Scale Generative Pretraining for Transferable Motor Control](https://mlsanity.com/paper/2606.29148/) (2026)
- [Recovering Physically Plausible Human-Object Interactions from Monocular Videos](https://mlsanity.com/paper/2606.05359/) (2026)

## Abstract (arXiv)

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.