Human 3D keypoints via spatial uncertainty modeling

12/18/2020
by   Francis Williams, et al.
5

We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint. Our technique employs a principled approach to modelling spatial uncertainty inspired from techniques in robust statistics. Furthermore, our pipeline requires no 3D ground truth labels, relying instead on (possibly noisy) 2D image-level keypoints. Our method achieves near state-of-the-art performance on Human3.6m while being efficient to evaluate and straightforward to

READ FULL TEXT
research
11/05/2020

Can Human Sex Be Learned Using Only 2D Body Keypoint Estimations?

In this paper, we analyze human male and female sex recognition problem ...
research
07/03/2023

Shi-NeSS: Detecting Good and Stable Keypoints with a Neural Stability Score

Learning a feature point detector presents a challenge both due to the a...
research
07/22/2015

Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization

We present a simple deep learning framework to simultaneously predict ke...
research
01/13/2022

Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching

We develop new statistics for robustly filtering corrupted keypoint matc...
research
10/19/2021

On Coordinate Decoding for Keypoint Estimation Tasks

A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap...
research
05/18/2023

XFormer: Fast and Accurate Monocular 3D Body Capture

We present XFormer, a novel human mesh and motion capture method that ac...
research
12/12/2021

Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species

Current non-rigid object keypoint detectors perform well on a chosen kin...

Please sign up or login with your details

Forgot password? Click here to reset