Parametric Model-Based 3D Human Shape and Pose Estimation from Multiple Views

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Bibtex

@inproceedings{99328ca15892473a89d6af54ee8a2159,
title = "Parametric Model-Based 3D Human Shape and Pose Estimation from Multiple Views",
abstract = "Human body pose and shape estimation is an important and challenging task in computer vision. This paper presents a novel method for estimating 3D human body pose and shape from several RGB images, using detected joint positions in the images and based on a parametric human body model. Firstly, the 2D joint points of the RGB images are estimated using a deep neural network, which provides a strong prior on the pose. Then, an energy function is constructed based on the 2D joint points in the RGB images and a parametric human body model. By minimizing the energy function, the pose, shape and camera parameters are obtained. The main contribution of the method over previous work, is that the optimization is based on several images simultaneously using only estimated joint positions in the images. We have performed experiments on both synthetic and real image data-sets, that demonstrate that our method can reconstruct 3D human bodies with better accuracy than previous single view methods.",
keywords = "Camera, Human body, Parametric model, Pose and shape estimation, RGB images",
author = "Zhongguo Li and Anders Heyden and Magnus Oskarsson",
year = "2019",
doi = "10.1007/978-3-030-20205-7_28",
language = "English",
isbn = "9783030202040",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "336--347",
editor = "Michael Felsberg and Per-Erik Forss{\'e}n and Jonas Unger and Ida-Maria Sintorn",
booktitle = "Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings",
address = "Germany",
note = "21st Scandinavian Conference on Image Analysis, SCIA 2019 ; Conference date: 11-06-2019 Through 13-06-2019",

}