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

Estimating 3D human pose and shape from a single image is highlyunder-constrained. To address this ambiguity, we propose a novel prior, namelykinematic dictionary, which explicitly regularizes the solution space ofrelative 3D rotations of human joints in the kinematic tree. Integrated with astatistical human model and a deep neural network, our method achievesend-to-end 3D reconstruction without the need of using any shape annotationsduring the training of neural networks. The kinematic dictionary bridges thegap between in-the-wild images and 3D datasets, and thus facilitates end-to-endtraining across all types of datasets. The proposed method achieves competitiveresults on large-scale datasets including Human3.6M, MPI-INF-3DHP, and LSP,while running in real-time given the human bounding boxes.

Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape Reconstruction