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

Model-based approaches to 3D hand tracking have been shown to perform well ina wide range of scenarios. However, they require initialisation and cannotrecover easily from tracking failures that occur due to fast hand motions.Data-driven approaches, on the other hand, can quickly deliver a solution, butthe results often suffer from lower accuracy or missing anatomical validitycompared to those obtained from model-based approaches. In this work we proposea hybrid approach for hand pose estimation from a single depth image. First, alearned regressor is employed to deliver multiple initial hypotheses for the 3Dposition of each hand joint. Subsequently, the kinematic parameters of a 3Dhand model are found by deliberately exploiting the inherent uncertainty of theinferred joint proposals. This way, the method provides anatomically valid andaccurate solutions without requiring manual initialisation or suffering fromtrack losses. Quantitative results on several standard datasets demonstratethat the proposed method outperforms state-of-the-art representatives of themodel-based, data-driven and hybrid paradigms.

Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties