Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models

Ilic, Andela; Jiang, Jiaxi; Streli, Paul; Liu, Xintong; Holz, Christian

doi:10.24963/ijcai.2025/135

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models

Andela Ilic, Jiaxi Jiang, Paul Streli, Xintong Liu, Christian Holz

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1206-1214.

https://doi.org/10.24963/ijcai.2025/135

Keywords

Computer Vision: CV: Biometrics, face, gesture and pose recognition
Computer Vision: CV: Motion and tracking

Abstract

Motion capture using sparse inertial sensors has shown great promise due to its portability and lack of occlusion issues compared to camera-based tracking.

Existing approaches typically assume that IMU sensors are tightly attached to the human body.

However, this assumption often does not hold in real-world scenarios.

In this paper, we present a new task of full-body human pose estimation using sparse, loosely attached IMU sensors.

To solve this task, we simulate IMU recordings from an existing garment-aware human motion dataset.

We developed transformer-based diffusion models to synthesize loose IMU data and estimate human poses based on this challenging loose IMU data.

In addition, we show that incorporating garment-related parameters while training the model on simulated loose data effectively maintains expressiveness and enhances the ability to capture variations introduced by looser or tighter garments.

Experiments show that our proposed diffusion methods trained on simulated and synthetic data outperformed the state-of-the-art methods quantitatively and qualitatively, opening up a promising direction for future research.

Andela Ilic, Jiaxi Jiang, Paul Streli, Xintong Liu, and Christian Holz. 2025. Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 1206–1214. https://doi.org/10.24963/ijcai.2025/135

@inproceedings{ijcai2025p135,
  title     = {Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models},
  author    = {Ilic, Andela and Jiang, Jiaxi and Streli, Paul and Liu, Xintong and Holz, Christian},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {1206--1214},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/135},
  url       = {https://doi.org/10.24963/ijcai.2025/135},
}

TY  - CPAPER
TI  - Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models
AU  - Ilic, Andela
AU  - Jiang, Jiaxi
AU  - Streli, Paul
AU  - Liu, Xintong
AU  - Holz, Christian
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 1206
EP  - 1214
DO  - 10.24963/ijcai.2025/135
UR  - https://doi.org/10.24963/ijcai.2025/135
ER  -