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

In-the-wild human pose estimation has a huge potential for various fields,ranging from animation and action recognition to intention recognition andprediction for autonomous driving. The current state-of-the-art is focused onlyon RGB and RGB-D approaches for predicting the 3D human pose. However, notusing precise LiDAR depth information limits the performance and leads to veryinaccurate absolute pose estimation. With LiDAR sensors becoming moreaffordable and common on robots and autonomous vehicle setups, we propose anend-to-end architecture using RGB and LiDAR to predict the absolute 3D humanpose with unprecedented precision. Additionally, we introduce aweakly-supervised approach to generate 3D predictions using 2D pose annotationsfrom PedX [1]. This allows for many new opportunities in the field of 3D humanpose estimation.

HPERL: 3D Human Pose Estimation from RGB and LiDAR