# SIM2VR: Towards Automated Biomechanical Testing in VR

arXiv: https://arxiv.org/abs/2404.17695 · HTML: https://mlsanity.com/paper/2404.17695/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI](https://mlsanity.com/paper/2508.16581/) (2025)
- [Log2Motion: Biomechanical Motion Synthesis from Touch Logs](https://mlsanity.com/paper/2601.21043/) (2026)
- [Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning](https://mlsanity.com/paper/2511.20591/) (2025)
- [MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning](https://mlsanity.com/paper/2602.15245/) (2026)
- [Robo-Saber: Generating and Simulating Virtual Reality Players](https://mlsanity.com/paper/2602.18319/) (2026)
- [Designing Fatigue-Aware VR Interfaces via Biomechanical Models](https://mlsanity.com/paper/2603.26031/) (2026)

## Abstract (arXiv)

Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed.