# Mocap Anywhere: Towards Pairwise-Distance based Motion Capture in the Wild (for the Wild)

arXiv: https://arxiv.org/abs/2601.19519 · HTML: https://mlsanity.com/paper/2601.19519/

## 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.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model](https://mlsanity.com/paper/2304.08577/) (2023)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars](https://mlsanity.com/paper/2209.09391/) (2022)
- [Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation](https://mlsanity.com/paper/2203.15720/) (2022)
- [EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors](https://mlsanity.com/paper/2305.01599/) (2023)
- [BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis](https://mlsanity.com/paper/2304.11118/) (2023)
- [Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling](https://mlsanity.com/paper/2308.08855/) (2023)
- [EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywhere](https://mlsanity.com/paper/2308.06493/) (2023)
- [Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera](https://mlsanity.com/paper/2401.00847/) (2024)
- [EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams](https://mlsanity.com/paper/2404.08640/) (2024)
- [SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images](https://mlsanity.com/paper/2006.03630/) (2020)
- [EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activity](https://mlsanity.com/paper/2502.18373/) (2025)
- [EgoHDM: An Online Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping System](https://mlsanity.com/paper/2409.00343/) (2024)
- [RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture](https://mlsanity.com/paper/2410.02788/) (2024)
- [UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units](https://mlsanity.com/paper/2505.09393/) (2025)

## 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.