Aug 24, 2023 · To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass. Specifically, POCO estimates both the 3D body pose and a per-sample variance.

We provide two versions of POCO: POCO-PARE and POCO-CLIFF. Note that POCO-CLIFF is more suitable for in-the-wild scenarios. To run a specific model, make sure to change the --cfg and --ckpt parameters accordingly i.e pare for POCO-PARE and cliff for POCO-CLIFF.

To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass. Specifically, POCO estimates both the 3D body pose and a per-sample variance.

To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass. Specifically, POCO estimates both the 3D body pose and a per-sample variance.

To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass. Specifically, POCO estimates both the 3D body pose and a per-sample variance.

Oct 15, 2023 · The authors introduce POCO (“POse and shape estimation with COnfidence”), a novel framework applicable to standard HPS methods to address these challenges. POCO extends these methods to estimate uncertainty.

We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose …

The regression of 3D Human Pose and Shape (HPS) from an image is becoming increasingly accurate. This makes the results useful for downstream tasks like human action recognition or 3D graphics. Yet, no regressor is per…

POCO: 3D Pose and Shape Estimation with Confidence **Supplementary Material** In this Supplementary-Material document, we provide more details about our method. Additionally, please see the Supplementary Video for a summary of the method and more visualizations of the results. 1. Regressor Network Architecture

We address these issues with POCO (“POse and shape estimation with COnfidence”), a novel framework that can be applied to common HPS methods, extending them to es-