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

2026-01-27

AI Summary

Achieving high-precision, camera-free full-body 3D motion capture in unconstrained outdoor environments remains challenging. This work proposes the first shape-agnostic, cross-species generalizable method based solely on sparse pairwise distance (PWD) measurements from wearable ultra-wideband (UWB) sensors, eliminating the need for subject-specific body parameters or environmental calibration. At its core is Wild-Poser (WiP), a lightweight, real-time Transformer architecture that directly predicts 3D joint positions from noisy PWD data and jointly learns to recover joint rotations. Experiments demonstrate that the approach achieves low joint position errors and high-fidelity 3D motion reconstruction on both human and non-human subjects, validating its practical potential as a low-cost, scalable solution for complex real-world scenarios.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• motion capture
• pairwise distance
• in-the-wild
• UWB
• 3D reconstruction

Innovation

• pairwise distance
• UWB sensors
• Transformer-based architecture
• in-the-wild motion capture
• shape-invariant