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 field of full-body reconstruction, the scarcity of annotated dataoften impedes the efficacy of prevailing methods. To address this issue, weintroduce FuRPE, a novel framework that employs part-experts and an ingeniouspseudo ground-truth selection scheme to derive high-quality pseudo labels.These labels, central to our approach, equip our network with the capability toefficiently learn from the available data. Integral to FuRPE is a uniqueexponential moving average training strategy and expert-derived featuredistillation strategy. These novel elements of FuRPE not only serve to furtherrefine the model but also to reduce potential biases that may arise frominaccuracies in pseudo labels, thereby optimizing the network's trainingprocess and enhancing the robustness of the model. We apply FuRPE to train bothtwo-stage and fully convolutional single-stage full-body reconstructionnetworks. Our exhaustive experiments on numerous benchmark datasets illustratea substantial performance boost over existing methods, underscoring FuRPE'spotential to reshape the state-of-the-art in full-body reconstruction.

FuRPE: Learning Full-body Reconstruction from Part Experts