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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this work we study the use of 3D hand poses to recognize first-persondynamic hand actions interacting with 3D objects. Towards this goal, wecollected RGB-D video sequences comprised of more than 100K frames of 45 dailyhand action categories, involving 26 different objects in several handconfigurations. To obtain hand pose annotations, we used our own mo-cap systemthat automatically infers the 3D location of each of the 21 joints of a handmodel via 6 magnetic sensors and inverse kinematics. Additionally, we recordedthe 6D object poses and provide 3D object models for a subset of hand-objectinteraction sequences. To the best of our knowledge, this is the firstbenchmark that enables the study of first-person hand actions with the use of3D hand poses. We present an extensive experimental evaluation of RGB-D andpose-based action recognition by 18 baselines/state-of-the-art approaches. Theimpact of using appearance features, poses, and their combinations aremeasured, and the different training/testing protocols are evaluated. Finally,we assess how ready the 3D hand pose estimation field is when hands areseverely occluded by objects in egocentric views and its influence on actionrecognition. From the results, we see clear benefits of using hand pose as acue for action recognition compared to other data modalities. Our dataset andexperiments can be of interest to communities of 3D hand pose estimation, 6Dobject pose, and robotics as well as action recognition.

First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations