Fast Neural Inverse Kinematics on Human Body Motions

TL;DR

This paper introduces a fast neural inverse kinematics framework for real-time human motion capture from 3D keypoints, addressing computational challenges in markerless motion capture systems.

Contribution

It presents a novel neural network architecture and training approach that enable real-time inference for markerless human motion capture.

Findings

Achieves real-time performance in human motion capture

Demonstrates high accuracy and reliability

Supports flexible and cost-effective motion analysis

Abstract

Markerless motion capture enables the tracking of human motion without requiring physical markers or suits, offering increased flexibility and reduced costs compared to traditional systems. However, these advantages often come at the expense of higher computational demands and slower inference, limiting their applicability in real-time scenarios. In this technical report, we present a fast and reliable neural inverse kinematics framework designed for real-time capture of human body motions from 3D keypoints. We describe the network architecture, training methodology, and inference procedure in detail. Our framework is evaluated both qualitatively and quantitatively, and we support key design decisions through ablation studies.