Markerless hand-tracking has become increasingly common on commercially available virtual and mixed reality headsets to improve the naturalness of interaction and immersivity of virtual environments. However, there has been limited examination of the performance of markerless hand-tracking on commercial head-mounted displays. Here, we propose an evaluation methodology that leverages a robotic manipulator to measure the positional accuracy, jitter, and latency of such systems and provides a standardized characterization framework of markerless hand-tracking. We apply this methodology to evaluate the hand-tracking performance of two recent mixed reality devices from Meta: the Quest Pro and Quest 3. Results demonstrate the influence of proximity to the headset, rotation of hand, and joint selected as the tracking feature on hand-tracking performance. We found that hand-tracking error and jitter were lowest for both headsets in conditions where the knuckle was the tracking point compared to the fingertip. Regarding positional accuracy, in best-performing conditions, the Quest Pro outperformed the Quest 3 with 1.22 cm of average error compared to 1.73 cm. The opposite result was true concerning jitter, with results of 1.77 cm and 1.11 cm for the Quest Pro and Quest 3, respectively. We found latency highly variable for the Quest Pro (15.8 - 229.2 ms) and Quest 3 (14.4 - 220.5 ms). This work provides a testing framework for highly systematic and repeatable performance measurements of markerless hand-tracking systems embedded in headsets.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TVCG.2025.3549182DOI Listing

Publication Analysis

Top Keywords

markerless hand-tracking
20
quest pro
20
pro quest
12
quest
10
hand-tracking
8
meta quest
8
reality headsets
8
mixed reality
8
positional accuracy
8
hand-tracking performance
8

Similar Publications

Markerless hand-tracking has become increasingly common on commercially available virtual and mixed reality headsets to improve the naturalness of interaction and immersivity of virtual environments. However, there has been limited examination of the performance of markerless hand-tracking on commercial head-mounted displays. Here, we propose an evaluation methodology that leverages a robotic manipulator to measure the positional accuracy, jitter, and latency of such systems and provides a standardized characterization framework of markerless hand-tracking.

View Article and Find Full Text PDF

Recording and quantifying hand and finger movement is essential for understanding the neuromechanical control of the hand. Typically, kinematics are collected through marker-based optoelectronic motion capture systems. However, marker-based systems are time-consuming to setup, expensive, and cumbersome, especially for finger tracking.

View Article and Find Full Text PDF

Verification of Criterion-Related Validity for Developing a Markerless Hand Tracking Device.

Biomimetics (Basel)

July 2024

Department of Rehabilitation, Graduate School of Health Sciences, Saitama Prefectural University, Saitama 343-8540, Japan.

Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand movements. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit's dependency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe.

View Article and Find Full Text PDF

Design of Virtual Hands for Natural Interaction in the Metaverse.

Sensors (Basel)

January 2024

Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 Valencia, Spain.

The emergence of the Metaverse is raising important questions in the field of human-machine interaction that must be addressed for a successful implementation of the new paradigm. Therefore, the exploration and integration of both technology and human interaction within this new framework are needed. This paper describes an innovative and technically viable proposal for virtual shopping in the fashion field.

View Article and Find Full Text PDF

Fast prediction in marmoset reach-to-grasp movements for dynamic prey.

Curr Biol

June 2023

Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14611, USA. Electronic address:

Primates have evolved sophisticated, visually guided reaching behaviors for interacting with dynamic objects, such as insects, during foraging. Reaching control in dynamic natural conditions requires active prediction of the target's future position to compensate for visuo-motor processing delays and to enhance online movement adjustments. Past reaching research in non-human primates mainly focused on seated subjects engaged in repeated ballistic arm movements to either stationary targets or targets that instantaneously change position during the movement.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!