Model

IMUdrape

text → motion → cloth-simulated garment-mounted virtual IMU for HAR

Year
2026
Form factor
Garment
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
Under reviewCatalog paper date: Not reportedAdded to atlas: Nov 5, 2026Source

Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

IMUdrape extends the IMUGPT text→virtual-IMU line from body-mounted to garment-mounted sensors. Text prompts drive HY-Motion-1.0 motion generation; a matched garment is sewn and simulated on the moving body, and virtual IMUs are read off the cloth. Every simulation yields paired garment and body signals.

Signals
IMU, orientation
Pretraining data
No pretraining; synthetic data generator
Form factor
Garment
Year
2026
Architecture

Pipeline: LLM text prompts → HY-Motion-1.0 text-to-motion → body mesh → sewing-pattern garment + physics-based cloth simulation → virtual garment-mounted IMU (paired with body-mounted IMU)

License

CC BY 4.0 (paper); MIT (code repository); code not yet released

At a glance
Type
Task model
Modalities
IMUorientation
Form factor
Garment
Parameters
Not reported
Architecture
Pipeline: LLM text prompts → HY-Motion-1.0 text-to-motion → body mesh → sewing-pattern garment + physics-based cloth simulation → virtual garment-mounted IMU (paired with body-mounted IMU)
Venue
arXiv 2611.02393v1 (cs.CV, 2026-11-02), IMWUT submission (under review); CC BY 4.0
Year
2026
DOI
Not reported
Code
Not released
Weights
Not released
Licence
CC BY 4.0 (paper); MIT (code repository); code not yet released
Caveats
Not reported
Datasets
Not reported
Added
Nov 5, 2026
Updated
Nov 5, 2026

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 fromarXiv· arXiv metadata (abstracts are released under CC0)

How it works

How IMUdrape works: Input signals → Model → Downstream tasksInput signals: IMU (Accelerometer, 3-axis); orientation. Pretraining data: No pretraining; synthetic data generator. Model: IMUdrape (Language-aligned · Masked reconstruction). Downstream tasks: Activity recognition.Input signalsIMU: Accelerometer, 3-axisxyzIMUAccelerometer, 3-axisorientationorientationPretraining data: No pretraining; synthetic data generatorPretraining dataNo pretraining; synthetic data generatorModelIMUdrapeLanguage-aligned · Masked…Downstream tasksActivity recognitionActivity recognition
Pretraining data
No pretraining; synthetic data generator
Architecture
Pipeline: LLM text prompts → HY-Motion-1.0 text-to-motion → body mesh → sewing-pattern garment + physics-based cloth simulation → virtual garment-mounted IMU (paired with body-mounted IMU)
Inputs
IMU, orientation
Approach
synthetic IMU · text to IMU · cloth simulation · garment · HAR · pose estimation

Datasets used

No public datasets linked yet.

Papers· 2

Authors· 2

Cite this model

Cite the publication recorded for this model. Check it against the publisher before submitting.

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}
}

Source metadata: 2026

Record updated Nov 5, 2026

History

  1. Added to the ChatPPG research hub