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

Training a 3D human keypoint detector from point clouds in a supervisedmanner requires large volumes of high quality labels. While it is relativelyeasy to capture large amounts of human point clouds, annotating 3D keypoints isexpensive, subjective, error prone and especially difficult for long-tail cases(pedestrians with rare poses, scooterists, etc.). In this work, we proposeGC-KPL - Geometry Consistency inspired Key Point Leaning, an approach forlearning 3D human joint locations from point clouds without human labels. Weachieve this by our novel unsupervised loss formulations that account for thestructure and movement of the human body. We show that by training on a largetraining set from Waymo Open Dataset without any human annotated keypoints, weare able to achieve reasonable performance as compared to the fully supervisedapproach. Further, the backbone benefits from the unsupervised training and isuseful in downstream fewshot learning of keypoints, where fine-tuning on only10 percent of the labeled training data gives comparable performance tofine-tuning on the entire set. We demonstrated that GC-KPL outperforms by alarge margin over SoTA when trained on entire dataset and efficiently leverageslarge volumes of unlabeled data.

3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels