# Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

## TL;DR

This paper presents a novel method for capturing two-hand motion using a single RGB-D camera by integrating a generative model, salient points, and collision detection, addressing challenges like occlusion and finger similarity.

## Contribution

It introduces a framework that enables two-hand tracking with a single inexpensive RGB-D sensor, combining generative modeling and salient point detection for improved accuracy.

## Key findings

- Successfully captures complex hand interactions
- Outperforms existing methods on challenging sequences
- Uses only a single RGB-D camera for effective tracking

## Abstract

Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.