Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser

• URL: http://arxiv.org/abs/2601.01360v1
• Date: Sun, 04 Jan 2026 04:08:21 GMT
• Title: Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser
• Abstract summary: We propose GID (Garment Inertial Denoiser), a lightweight, plug-and-play Transformer that factorizes loosewear MoCap into three stages.<n>GID uses a location-aware expert architecture, where a shared-temporal backbone models global motion while per-IMU expert heads specialize in local dynamics.<n>Experiments show that GID enables accurate, real-time denoising from single-user training and generalizes across unseen users, motions, and garments.
• Score: 24.814697238576088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wearable inertial motion capture (MoCap) provides a portable, occlusion-free, and privacy-preserving alternative to camera-based systems, but its accuracy depends on tightly attached sensors - an intrusive and uncomfortable requirement for daily use. Embedding IMUs into loose-fitting garments is a desirable alternative, yet sensor-body displacement introduces severe, structured, and location-dependent corruption that breaks standard inertial pipelines. We propose GID (Garment Inertial Denoiser), a lightweight, plug-and-play Transformer that factorizes loose-wear MoCap into three stages: (i) location-specific denoising, (ii) adaptive cross-wear fusion, and (iii) general pose prediction. GID uses a location-aware expert architecture, where a shared spatio-temporal backbone models global motion while per-IMU expert heads specialize in local garment dynamics, and a lightweight fusion module ensures cross-part consistency. This inductive bias enables stable training and effective learning from limited paired loose-tight IMU data. We also introduce GarMoCap, a combined public and newly collected dataset covering diverse users, motions, and garments. Experiments show that GID enables accurate, real-time denoising from single-user training and generalizes across unseen users, motions, and garment types, consistently improving state-of-the-art inertial MoCap methods when used as a drop-in module.

Related papers

• Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration [50.18488308004802]
  Sen-Cap is a 3D motion capture framework that integrates multi-modal data from LiDAR and camera.<n>Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion.<n>This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios.
  arXiv  Detail & Related papers  (2026-08-03T14:16:18Z)
• Physical Self-Supervised Learning: IMU Sensing without Manual Labels [10.122645401488084]
  Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is limited by labeled data and poor robustness to heterogeneous devices, placements, and users.<n>We propose physical self-supervised learning, an autoencoder-style paradigm for label-free IMU sensing.<n>We replace the conventional neural decoder with an auto-adaptive physics decoder, a learnable family of kinematic equations that enforces explicit physical structure while adapting across environments.
  arXiv  Detail & Related papers  (2026-07-20T14:01:10Z)
• Towards Real-World Wearable Motion Reconstruction [68.1355376294881]
  We present a large-scale dataset synchronizing consumer-grade sensors with ground-truth 3D motion.<n>Second, we propose WHIP, a baseline generative model that reconstructs motion from arbitrary subsets of available sensors.<n>Third, we conduct a systematic study of sensor complementarity, quantifying how different modalities complement one another.
  arXiv  Detail & Related papers  (2026-07-08T11:56:02Z)
• Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation [58.33732779555114]
  We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities.<n>We propose Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation (SpectraMB)<n>SpectraMB achieves the best results in most evaluation settings and exhibits improved robustness under noisy interactions.
  arXiv  Detail & Related papers  (2026-06-01T15:58:13Z)
• Deformable Gaussian Occupancy: Decoupling Rigid and Nonrigid Motion with Factorized Distillation [81.44955493339835]
  DeGO is a deformable Gaussian occupancy framework that unifies decoupled Gaussian deformation with factorized 4D foundation-model distillation.<n> Experiments on the Occ3D-NuScenes benchmark demonstrate that our method achieves state-of-the-art performance under weak supervision.
  arXiv  Detail & Related papers  (2026-05-27T15:09:55Z)
• AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild [10.47918824187894]
  We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling.<n>AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals.<n>We evaluate AnyMo on three complementary tasks: zero-shot activity recognition, cross-modal retrieval, and wearable IMU motion captioning.
  arXiv  Detail & Related papers  (2026-05-21T16:52:10Z)
• DreamVideo-Omni: Omni-Motion Controlled Multi-Subject Video Customization with Latent Identity Reinforcement Learning [82.28800081483224]
  We present DreamVideo- Omni, a unified framework enabling multisubject customization with omni-motion control.<n>To resolve multi-subject ambiguity, we introduce group and role embeddings to explicitly anchor motion signals to specific identities.<n>This provides motion-aware identity rewards in the latent space, prioritizing identity preservation aligned with human preferences.
  arXiv  Detail & Related papers  (2026-03-12T17:59:12Z)
• ModalPatch: A Plug-and-Play Module for Robust Multi-Modal 3D Object Detection under Modality Drop [23.33231492677983]
  We introduce ModalPatch, a plug-and-play module designed to enable robust detection under arbitrary modality-drop scenarios.<n>Technically, ModalPatch leverages the temporal nature of sensor data for perceptual continuity, using a history-based module to predict and compensate for transiently unavailable features.<n>Extensive experiments show that ModalPatch consistently enhances both robustness and accuracy of state-of-the-art 3D object detectors under diverse modality-drop conditions.
  arXiv  Detail & Related papers  (2026-03-03T00:09:00Z)
• Steering and Rectifying Latent Representation Manifolds in Frozen Multi-modal LLMs for Video Anomaly Detection [52.5174167737992]
  Video anomaly detection (VAD) aims to identify abnormal events in videos.<n>We propose SteerVAD, which advances MLLM-based VAD by shifting from passively reading to actively steering and rectifying internal representations.<n>Our method achieves state-of-the-art performance among tuning-free approaches requiring only 1% of training data.
  arXiv  Detail & Related papers  (2026-02-27T13:48:50Z)
• MobiDiary: Autoregressive Action Captioning with Wearable Devices and Wireless Signals [20.30554810750095]
  Human Activity Recognition (HAR) in smart homes is critical for health monitoring and assistive living.<n>We present MobiDiary, a framework that generates natural language descriptions of daily activities directly from heterogeneous physical signals.
  arXiv  Detail & Related papers  (2026-01-13T04:16:17Z)
• Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation [56.36237936346563]
  Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks.<n>Due to data privacy regulations, cloud-based FMs cannot directly access private edge data.<n>We introduce Practical Semi-Supervised Federated Learning (PSSFL), where edge devices hold only unlabeled, low-resolution data.<n>Our work paves the way for scalable and privacy-preserving FM adaptation in federated scenarios.
  arXiv  Detail & Related papers  (2025-08-22T17:47:02Z)
• MeMoSORT: Memory-Assisted Filtering and Motion-Adaptive Association Metric for Multi-Person Tracking [3.3959720271630216]
  We propose MeMoSORT, a simple, online, and real-time MOT tracker with two key innovations.<n>MeMoSORT uses memory-augmented neural networks to compensate for mismatches between assumed and actual object motion.<n>Experiments on DanceTrack and SportsMOT show that MeMoSORT achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-08-13T13:26:31Z)
• Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models [25.20942802233326]
  Garment Inertial Poser (GaIP) is a method for estimating full-body poses from sparse and loosely attached IMU sensors.<n>Our experiments show that our diffusion methods trained on simulated and synthetic data outperform state-of-the-art inertial full-body pose estimators.
  arXiv  Detail & Related papers  (2025-06-18T09:16:36Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [92.4205087439928]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose the Self-supervised Transfer (PST) and the FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.<n>This combined approach enables FUSE to construct a universal image-event that only requires lightweight decoder adaptation for target datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation [10.634236058278722]
  Motion data captured by sensors on different body parts contains both local and global motion information. We propose a multi-device deep learning framework named Suite-IN, aggregating motion data from Apple Suite for inertial navigation.
  arXiv  Detail & Related papers  (2024-11-12T14:23:52Z)
• CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes [56.52618054240197]
  We propose a novel, condition-aware multimodal fusion approach for robust semantic perception of driving scenes.<n>Our method, CAFuser, uses an RGB camera input to classify environmental conditions and generate a Condition Token.<n>Our model significantly improves robustness and accuracy, especially in adverse-condition scenarios.
  arXiv  Detail & Related papers  (2024-10-14T17:56:20Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics [74.1720528573331]
  Unsupervised monocular depth and ego-motion estimation has drawn extensive research attention in recent years. We propose DynaDepth, a novel scale-aware framework that integrates information from vision and IMU motion dynamics. We validate the effectiveness of DynaDepth by conducting extensive experiments and simulations on the KITTI and Make3D datasets.
  arXiv  Detail & Related papers  (2022-07-11T07:50:22Z)
• HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions [41.56735523771541]
  We present a light-weight, hybrid mocap technique called HybridCap. It augments the camera with only 4 Inertial Measurement Units (IMUs) in a learning-and-optimization framework. It can robustly handle challenging movements ranging from fitness actions to Latin dance.
  arXiv  Detail & Related papers  (2022-03-17T12:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.