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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper we present a high fidelity and articulated 3D human foot model.The model is parameterised by a disentangled latent code in terms of shape,texture and articulated pose. While high fidelity models are typically createdwith strong supervision such as 3D keypoint correspondences orpre-registration, we focus on the difficult case of little to no annotation. Tothis end, we make the following contributions: (i) we develop a Foot ImplicitNeural Deformation field model, named FIND, capable of tailoring explicitmeshes at any resolution i.e. for low or high powered devices; (ii) an approachfor training our model in various modes of weak supervision with progressivelybetter disentanglement as more labels, such as pose categories, are provided;(iii) a novel unsupervised part-based loss for fitting our model to 2D imageswhich is better than traditional photometric or silhouette losses; (iv)finally, we release a new dataset of high resolution 3D human foot scans,Foot3D. On this dataset, we show our model outperforms a strong PCAimplementation trained on the same data in terms of shape quality and partcorrespondences, and that our novel unsupervised part-based loss improvesinference on images.

FIND: An Unsupervised Implicit 3D Model of Articulated Human Feet