# Log2Motion: Biomechanical Motion Synthesis from Touch Logs

arXiv: https://arxiv.org/abs/2601.21043 · HTML: https://mlsanity.com/paper/2601.21043/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 107 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 20 of 20)

- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [AndroidEnv: A Reinforcement Learning Platform for Android](https://mlsanity.com/paper/2105.13231/) (2021)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation](https://mlsanity.com/paper/1904.13041/) (2019)
- [SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation](https://mlsanity.com/paper/2307.03716/) (2023)
- [Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity](https://mlsanity.com/paper/2011.07105/) (2020)
- [Chartist: Task-driven Eye Movement Control for Chart Reading](https://mlsanity.com/paper/2502.03575/) (2025)
- [SIM2VR: Towards Automated Biomechanical Testing in VR](https://mlsanity.com/paper/2404.17695/) (2024)
- [General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study](https://mlsanity.com/paper/2312.12111/) (2023)
- [What and How long: Prediction of Mobile App Engagement](https://mlsanity.com/paper/2106.01490/) (2021)
- [On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions](https://mlsanity.com/paper/2301.02065/) (2023)
- [What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation](https://mlsanity.com/paper/2503.02571/) (2025)
- [Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI](https://mlsanity.com/paper/2508.16581/) (2025)
- [Arnold: A multi-task, multi-embodiment muscle transformer policy](https://mlsanity.com/paper/2508.18066/) (2025)
- [Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces](https://mlsanity.com/paper/2307.06089/) (2023)
- [Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control](https://mlsanity.com/paper/2503.17993/) (2025)
- [Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI](https://mlsanity.com/paper/2508.13788/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Benchmarking Framework for Context-aware XR Interfaces](https://mlsanity.com/paper/2609.30466/) (2026)

## Abstract (arXiv)

Touch data from mobile devices are collected at scale but reveal little about the interactions that produce them. While biomechanical simulations can illuminate motor control processes, they have not yet been developed for touch interactions. To close this gap, we propose a novel computational problem: synthesizing plausible motion directly from logs. Our key insight is a reinforcement learning-driven musculoskeletal forward simulation that generates biomechanically plausible motion sequences consistent with events recorded in touch logs. We achieve this by integrating a software emulator into a physics simulator, allowing biomechanical models to manipulate real applications in real-time. Log2Motion produces rich syntheses of user movements from touch logs, including estimates of motion, speed, accuracy, and effort. We assess the plausibility of generated movements by comparing against human data from a motion capture study and prior findings, and demonstrate Log2Motion in a large-scale dataset. Biomechanical motion synthesis provides a new way to understand log data, illuminating the ergonomics and motor control underlying touch interactions.