English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Real-time simultaneous tracking of hands manipulating and interacting withexternal objects has many potential applications in augmented reality, tangiblecomputing, and wearable computing. However, due to difficult occlusions, fastmotions, and uniform hand appearance, jointly tracking hand and object pose ismore challenging than tracking either of the two separately. Many previousapproaches resort to complex multi-camera setups to remedy the occlusionproblem and often employ expensive segmentation and optimization steps whichmakes real-time tracking impossible. In this paper, we propose a real-timesolution that uses a single commodity RGB-D camera. The core of our approach isa 3D articulated Gaussian mixture alignment strategy tailored to hand-objecttracking that allows fast pose optimization. The alignment energy uses novelregularizers to address occlusions and hand-object contacts. For addedrobustness, we guide the optimization with discriminative part classificationof the hand and segmentation of the object. We conducted extensive experimentson several existing datasets and introduce a new annotated hand-object dataset.Quantitative and qualitative results show the key advantages of our method:speed, accuracy, and robustness.

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input