Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

Tome, D, Russell, Christopher and Agapito, L (2017) Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image In: CVPR 2017, 21 - 26 July 2017, Honolulu, Hawaii.

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Abstract

We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains stateof-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Tome, D Russell, Christopher cr0040@surrey.ac.uk Agapito, L
Date :	26 July 2017
Funders :	EPSRC
Copyright Disclaimer :	This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :	Organisation
Depositing User :	Melanie Hughes
Date Deposited :	09 Aug 2017 12:38
Last Modified :	11 Dec 2018 11:23
URI:	http://epubs.surrey.ac.uk/id/eprint/841869

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