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/

Zendy Plus

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

Zendy Tools

Zendy Open

In this paper we address the task of the comparison and the classification of3D shape sequences of human. The non-linear dynamics of the human motion andthe changing of the surface parametrization over the time make this task verychallenging. To tackle this issue, we propose to embed the 3D shape sequencesin an infinite dimensional space, the space of varifolds, endowed with an innerproduct that comes from a given positive definite kernel. More specifically,our approach involves two steps: 1) the surfaces are represented as varifolds,this representation induces metrics equivariant to rigid motions and invariantto parametrization; 2) the sequences of 3D shapes are represented by Grammatrices derived from their infinite dimensional Hankel matrices. The problemof comparison of two 3D sequences of human is formulated as a comparison of twoGram-Hankel matrices. Extensive experiments on CVSSP3D and Dyna datasets showthat our method is competitive with state-of-the-art in 3D human sequencemotion retrieval. Code for the experiments is available athttps://github.com/CRISTAL-3DSAM/HumanComparisonVarifolds.

3D Shape Sequence of Human Comparison and Classification using Current and Varifolds