Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Qinzhuan Qian
Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
HPERL: 3D Human Pose Estimation from RGB and LiDAR
HPERL: 3D Human Pose Estimation from RGB and LiDAR
International Conference on Pattern Recognition. International Conference on Pattern Recognition (ICPR-2020) January 10-15 Milan Italy IEEE 2021 .
- Abstract:
- n-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB and RGB-D approaches for predicting the 3D human pose. However, not using precise LiDAR depth information limits the performance and leads to very inaccurate absolute pose estimation. With LiDAR sensors becoming more affordable and common on robots and autonomous vehicle setups, we propose an end-to-end architecture using RGB and LiDAR to predict the absolute 3D human pose with unprecedented precision. Additionally, we introduce a weakly-supervised approach to generate 3D predictions using 2D pose annotations from PedX [1]. This allows for many new opportunities in the field of 3D human pose estimation.