HandSplatter: Automated Digital Goniometry from Neural Rendering

2026-08-10

AI Summary

This study addresses the limitations of current clinical approaches to assessing finger joint range of motion (ROM), which suffer from the inefficiency and subjectivity of manual goniometry and insufficient accuracy of existing digital methods. To overcome these challenges, the authors propose a novel 3D hand pose estimation pipeline that integrates neural rendering with multi-view synthesis. The method first extracts 2D keypoints, then generates multi-view images via neural rendering, and introduces a novel discrete density hill-climbing algorithm to refine and optimize the 3D-projected keypoints. This approach significantly enhances measurement accuracy and robustness, outperforming state-of-the-art software solutions in ROM assessment and offering clinicians a high-precision, automated, and objective evaluation tool.

Abstract

Hand and finger disorders are leading contributors to musculoskeletal disability, creating a clinical need for precise methods to quantify joint motion. Range of motion (ROM) serves as the metric for diagnosis, rehabilitation monitoring, and evaluating surgical outcomes. Currently, the goniometer is the standard tool for assessing finger flexion and extension. However, manual goniometry is labor-intensive and suffers from inconsistent inter-rater reliability due to variations in examiner technique. While digital alternatives exist, current software-based approaches often lack the necessary accuracy for clinical usage. To address these limitations, we present a novel pipeline for 3-D hand joint location and pose estimation using neural rendering. Unlike previous methods, our approach combines 2-D feature extraction with view synthesis to significantly improve accuracy and clinical viability. Furthermore, we introduce a discrete density hill climbing algorithm that facilitates the meaningful correction of projected landmarks in 3-D space. This system overcomes the inefficiencies of manual measurement and the inaccuracies of existing software, providing a robust tool for objective functional assessment.

Problem

• digital goniometry
• range of motion
• hand joint
• clinical assessment
• neural rendering

Innovation

• neural rendering
• 3D hand pose estimation
• digital goniometry
• view synthesis
• density hill climbing