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

Traditionally, monocular 3D human pose estimation employs a machine learningmodel to predict the most likely 3D pose for a given input image. However, asingle image can be highly ambiguous and induces multiple plausible solutionsfor the 2D-3D lifting step which results in overly confident 3D posepredictors. To this end, we propose \emph{DiffPose}, a conditional diffusionmodel, that predicts multiple hypotheses for a given input image. In comparisonto similar approaches, our diffusion model is straightforward and avoidsintensive hyperparameter tuning, complex network structures, mode collapse, andunstable training. Moreover, we tackle a problem of the common two-stepapproach that first estimates a distribution of 2D joint locations viajoint-wise heatmaps and consecutively approximates them based on first- orsecond-moment statistics. Since such a simplification of the heatmaps removesvalid information about possibly correct, though labeled unlikely, jointlocations, we propose to represent the heatmaps as a set of 2D joint candidatesamples. To extract information about the original distribution from thesesamples we introduce our \emph{embedding transformer} that conditions thediffusion model. Experimentally, we show that DiffPose slightly improves uponthe state of the art for multi-hypothesis pose estimation for simple poses andoutperforms it by a large margin for highly ambiguous poses.

DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models