Mocap Anywhere: Towards Pairwise-Distance based Motion Capture in the Wild (for the Wild)
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Abstract (arXiv)
We introduce a novel motion capture system that reconstructs full-body 3D motion using only sparse pairwise distance (PWD) measurements from body-mounted(UWB) sensors. Using time-of-flight ranging between wireless nodes, our method eliminates the need for external cameras, enabling robust operation in uncontrolled and outdoor environments. Unlike traditional optical or inertial systems, our approach is shape-invariant and resilient to environmental constraints such as lighting and magnetic interference. At the core of our system is Wild-Poser (WiP for short), a compact, real-time Transformer-based architecture that directly predicts 3D joint positions from noisy or corrupted PWD measurements, which can later be used for joint rotation reconstruction via learned methods. WiP generalizes across subjects of varying morphologies, including non-human species, without requiring individual body measurements or shape fitting. Operating in real time, WiP achieves low joint position error and demonstrates accurate 3D motion reconstruction for both human and animal subjects in-the-wild. Our empirical analysis highlights its potential for scalable, low-cost, and general purpose motion capture in real-world settings.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 68 references that are in the map. First posted on arXiv 27 Jan 2026.
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References · 22
- 2017Attention Is All You Needcited by 60,361
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2022Human Motion Diffusion Modelcited by 1,080
- 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 324
- 2021Character Controllers Using Motion VAEscited by 184
- 2021Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensorscited by 137
- 2023Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Modelcited by 107
- 2017A Survey of Indoor Localization Systems and Technologiescited by 78
- 2022QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatarscited by 77
- 2022Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generationcited by 76
- 2023EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensorscited by 48
10 more
- 2023BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesiscited by 45
- 2023Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modelingcited by 43
- 2023EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywherecited by 34
- 2024Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Cameracited by 31
- 2024EventEgo3D: 3D Human Motion Capture from Egocentric Event Streamscited by 29
- 2020SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Imagescited by 16
- 2025EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activitycited by 15
- 2024EgoHDM: An Online Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping Systemcited by 9
- 2024RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capturecited by 6
- 2025UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Unitscited by 5
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