Title:	ARPOSER: AUTOMATICALLY AUGMENTING MOBILE PICTURES WITH DIGITAL CHARACTERS IMITATING POSES
Author(s):	Gokcen Cimen, Christoph Maurhofer, Martin Guay and Robert W. Sumner
ISBN:	978-989-8533-79-1
Editors:	Katherine Blashki and Yingcai Xiao
Year:	2018
Edition:	Single
Keywords:	Augmented Reality, Pose Estimation, Intelligent Virtual Characters
Type:	Full Paper
First Page:	284
Last Page:	290
Language:	English
Cover:
Full Contents:	click to dowload
Paper Abstract:	We introduce AR Poser: a framework for posing with, or as a digital character. In this paper, we describe our first contribution to AR Poser: a technique for digital characters to recognize and automatically reproduce the same pose as a person in a picture (using only RGB information from a mobile device). 3D human pose estimation from RGB is an under-constrained and ambiguous problem that remains today an active field of study. Instead of addressing the general case of human pose estimation, we propose a solution that can be tailored to a specific scenario---such as entertainment poses for AR selfies. At the heart of our solution is a set of predefined poses (selfie poses) utilized to reduce ambiguities. In a nutshell, our method consists of two reliable steps: we first perform 2D pose estimation, and then perform a projection onto the 3D subspace to find the closest matching 3D pose. With our method, we are able to automatically create augmented reality selfies for a variety of different poses.
	Go Back