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

Continuous mid-air hand gesture recognition based on captured hand posestreams is fundamental for human-computer interaction, particularly in AR / VR.However, many of the methods proposed to recognize heterogeneous hand gesturesare tested only on the classification task, and the real-time low-latencygesture segmentation in a continuous stream is not well addressed in theliterature. For this task, we propose the On-Off deep Multi-View Multi-Taskparadigm (OO-dMVMT). The idea is to exploit multiple time-local views relatedto hand pose and movement to generate rich gesture descriptions, along withusing heterogeneous tasks to achieve high accuracy. OO-dMVMT extends theclassical MVMT paradigm, where all of the multiple tasks have to be active ateach time, by allowing specific tasks to switch on/off depending on whetherthey can apply to the input. We show that OO-dMVMT defines the new SotA oncontinuous/online 3D skeleton-based gesture recognition in terms of gestureclassification accuracy, segmentation accuracy, false positives, and decisionlatency while maintaining real-time operation.

OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation