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

We present a method for human pose tracking that is based on learningspatiotemporal relationships among joints. Beyond generating the heatmap of ajoint in a given frame, our system also learns to predict the offset of thejoint from a neighboring joint in the frame. Additionally, it is trained topredict the displacement of the joint from its position in the previous frame,in a manner that can account for possibly changing joint appearance, unlikeoptical flow. These relational cues in the spatial domain and temporal domainare inferred in a robust manner by attending only to relevant areas in thevideo frames. By explicitly learning and exploiting these joint relationships,our system achieves state-of-the-art performance on standard benchmarks forvarious pose tracking tasks including 3D body pose tracking in RGB video, 3Dhand pose tracking in depth sequences, and 3D hand gesture tracking in RGBvideo.

Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose