PaperarXiv:2611.02393
IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng & Plötz; arXiv 2611.02393)
arXiv 2611.02393v1 (cs.CV, 2026-11-02), IMWUT submission (under review); CC BY 4.0
(opens in a new tab)Leng, Zikang et al., "IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation", arXiv:2611.02393, CC BY 4.0
TL;DR
Generator: LLM prompts → HY-Motion-1.0 → SMPL body → garment sewing pattern + cloth simulation → garment IMU. Evaluation: HAR on MIG, LIP, FIP, GarMoCap; pose estimation on three of them.
- Form factor: garment
Abstract
Inertial sensors are moving into clothing, from sensorized sportswear to garments instrumented with hundreds of IMUs. Sewn into clothing, a sensor no longer moves rigidly with the body: loose fabric slides, twists and swings, so a garment-mounted sensor records the motion of the fabric as well as that of the body, and this extra motion differs from one garment to the next. Models for such sensors need training data recorded with the deployed garment, yet labeled data are scarce. Virtual IMU data generated from videos or text have eased this scarcity for body-attached sensors, but existing generators attach their virtual sensors rigidly to the body. We present IMUdrape, which generates garment-mounted virtual IMU data from text through physics-based cloth simulation. IMUdrape synthesizes motion from activity descriptions, reconstructs the target garment as a sewing pattern from photographs, places virtual IMUs at the deployment's real sensor positions, and simulates the garment with an intersection-free contact solver, so that every simulation yields paired garment and body signals. On four real datasets with clothing-mounted sensors, IMUdrape improves on existing virtual-data approaches for both activity recognition and full-body pose estimation. With one real subject, its data improve recognition macro-F1 by 7% to 46% over real data alone and by 2% to 11% over body-mounted virtual data from the same motion. When the garment is simulated on recorded motion, the pose error without any real training data falls by 30% to 33% relative to generated motion, and garment data remain 12% more accurate than body data. IMUdrape thus reduces the amount of real data that garment-mounted sensing requires.
Verbatim from arXiv (opens in a new tab) · arXiv metadata (abstracts are released under CC0)
Results on ChatPPG leaderboards
All leaderboardsNo results from this paper on ChatPPG leaderboards yet. Browse the leaderboards.
Tasks & signals
Tasks
Signals
Keywords
Models
How it works
Cite this paper
Leng, Z., & Plötz, T. (2026). IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng & Plötz; arXiv 2611.02393). arXiv 2611.02393v1 , IMWUT submission. https://arxiv.org/abs/2611.02393
Leng, Zikang and Thomas Plötz. 2026. "IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng & Plötz; arXiv 2611.02393)." arXiv 2611.02393v1 , IMWUT submission. https://arxiv.org/abs/2611.02393.
Leng, Z. and Plötz, T. (2026) 'IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng & Plötz; arXiv 2611.02393)', arXiv 2611.02393v1 , IMWUT submission. Available at: https://arxiv.org/abs/2611.02393
Leng, Zikang, and Thomas Plötz "IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng & Plötz; arXiv 2611.02393)." arXiv 2611.02393v1 , IMWUT submission, 2026. https://arxiv.org/abs/2611.02393.
Z. Leng and T. Plötz, "IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng & Plötz; arXiv 2611.02393)," arXiv 2611.02393v1 , IMWUT submission, 2026, [Online]. Available: https://arxiv.org/abs/2611.02393.
@misc{leng2026imudrape,
title = {{IMUdrape: Generating Garment-Mounted Virtual IMU Data from Text through Physics-Based Cloth Simulation (Leng \& Plötz; arXiv 2611.02393)}},
author = {Leng, Zikang and Plötz, Thomas},
year = {2026},
howpublished = {arXiv 2611.02393v1 , IMWUT submission},
eprint = {2611.02393},
archivePrefix = {arXiv}
}Added to the ChatPPG research hub
