scispace - formally typeset
H

Helge Rhodin

Researcher at University of British Columbia

Publications -  93
Citations -  5412

Helge Rhodin is an academic researcher from University of British Columbia. The author has contributed to research in topics: Pose & Motion capture. The author has an hindex of 25, co-authored 77 publications receiving 3792 citations. Previous affiliations of Helge Rhodin include École Polytechnique Fédérale de Lausanne & Max Planck Society.

Papers
More filters
Journal ArticleDOI

VNect: real-time 3D human pose estimation with a single RGB camera

TL;DR: In this paper, a fully-convolutional pose formulation was proposed to regress 2D and 3D joint positions jointly in real-time and does not require tightly cropped input frames.
Journal ArticleDOI

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

TL;DR: This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.
Proceedings ArticleDOI

Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision

TL;DR: In this article, a CNN-based approach for 3D human body pose estimation from single RGB images is proposed to address the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data.
Posted Content

Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision

TL;DR: A CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data is proposed.
Proceedings ArticleDOI

Learning Monocular 3D Human Pose Estimation from Multi-view Images

TL;DR: In this article, the authors propose to replace most of the annotations by the use of multiple views, at training time only, and train the system to predict the same pose in all views.