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

Most of the previous 3D human pose estimation work relied on the powerfulmemory capability of the network to obtain suitable 2D-3D mappings from thetraining data. Few works have studied the modeling of human posture deformationin motion. In this paper, we propose a new modeling method for human posedeformations and design an accompanying diffusion-based motion prior. Inspiredby the field of non-rigid structure-from-motion, we divide the task ofreconstructing 3D human skeletons in motion into the estimation of a 3Dreference skeleton, and a frame-by-frame skeleton deformation. A mixedspatial-temporal NRSfMformer is used to simultaneously estimate the 3Dreference skeleton and the skeleton deformation of each frame from 2Dobservations sequence, and then sum them to obtain the pose of each frame.Subsequently, a loss term based on the diffusion model is used to ensure thatthe pipeline learns the correct prior motion knowledge. Finally, we haveevaluated our proposed method on mainstream datasets and obtained superiorresults outperforming the state-of-the-art.

Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling