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

Expressive human pose and shape estimation (EHPS) unifies body, hands, andface motion capture with numerous applications. Despite encouraging progress,current state-of-the-art methods still depend largely on a confined set oftraining datasets. In this work, we investigate scaling up EHPS towards thefirst generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as thebackbone and training with up to 4.5M instances from diverse data sources. Withbig data and the large model, SMPLer-X exhibits strong performance acrossdiverse test benchmarks and excellent transferability to even unseenenvironments. 1) For the data scaling, we perform a systematic investigation on32 EHPS datasets, including a wide range of scenarios that a model trained onany single dataset cannot handle. More importantly, capitalizing on insightsobtained from the extensive benchmarking process, we optimize our trainingscheme and select datasets that lead to a significant leap in EHPScapabilities. 2) For the model scaling, we take advantage of visiontransformers to study the scaling law of model sizes in EHPS. Moreover, ourfinetuning strategy turn SMPLer-X into specialist models, allowing them toachieve further performance boosts. Notably, our foundation model SMPLer-Xconsistently delivers state-of-the-art results on seven benchmarks such asAGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF(62.3 mm PVE without finetuning). Homepage:https://caizhongang.github.io/projects/SMPLer-X/

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation