Parametric skeleton generation via gaussian mixture models

Chang Liu; Dezhao Luo; Yifei Zhang; Wei Ke; Fang Wan; Qixiang Ye

doi:10.1109/CVPRW.2019.00153

Parametric skeleton generation via gaussian mixture models

Chang Liu
, Dezhao Luo
, Yifei Zhang
, Wei Ke
, Fang Wan
, Qixiang Ye

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

We propose an efficient and effective control point extraction algorithm for parametric skeleton generation. The object skeleton pixels are predicted via an hourglass network and partitioned into skeleton branches using Gaussian Mixture Models. For each skeleton branch, a Bezier curve is utilized to generate the control points. The radius of the skeleton is computed by the distance between the border of the object and the Bezier curve. The branches are sorted by the area so that the parametric skeleton representation is unique. For the Parametric SkelNetOn competition, the proposed approach achieves the prediction score of 11793.89, which is in the first place on the performance leader-board.

Original language	English
Title of host publication	Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Publisher	IEEE Computer Society
Pages	1167-1171
Number of pages	5
ISBN (Electronic)	9781728125060
DOIs	https://doi.org/10.1109/CVPRW.2019.00153
State	Published - Jun 2019
Externally published	Yes
Event	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2019-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/Territory	United States
City	Long Beach
Period	16/06/19 → 20/06/19

Access to Document

10.1109/CVPRW.2019.00153

Cite this

Liu, C., Luo, D., Zhang, Y., Ke, W., Wan, F., & Ye, Q. (2019). Parametric skeleton generation via gaussian mixture models. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 1167-1171). Article 9025510 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00153

@inproceedings{be47925a5b0445b0a2b207ba76bbd90a,

title = "Parametric skeleton generation via gaussian mixture models",

abstract = "We propose an efficient and effective control point extraction algorithm for parametric skeleton generation. The object skeleton pixels are predicted via an hourglass network and partitioned into skeleton branches using Gaussian Mixture Models. For each skeleton branch, a Bezier curve is utilized to generate the control points. The radius of the skeleton is computed by the distance between the border of the object and the Bezier curve. The branches are sorted by the area so that the parametric skeleton representation is unique. For the Parametric SkelNetOn competition, the proposed approach achieves the prediction score of 11793.89, which is in the first place on the performance leader-board.",

author = "Chang Liu and Dezhao Luo and Yifei Zhang and Wei Ke and Fang Wan and Qixiang Ye",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 ; Conference date: 16-06-2019 Through 20-06-2019",

year = "2019",

month = jun,

doi = "10.1109/CVPRW.2019.00153",

language = "英语",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

publisher = "IEEE Computer Society",

pages = "1167--1171",

booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019",

}

Liu, C, Luo, D, Zhang, Y, Ke, W, Wan, F & Ye, Q 2019, Parametric skeleton generation via gaussian mixture models. in Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019., 9025510, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, IEEE Computer Society, pp. 1167-1171, 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019, Long Beach, United States, 16/06/19. https://doi.org/10.1109/CVPRW.2019.00153

Parametric skeleton generation via gaussian mixture models. / Liu, Chang; Luo, Dezhao; Zhang, Yifei et al.
Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society, 2019. p. 1167-1171 9025510 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parametric skeleton generation via gaussian mixture models

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AU - Luo, Dezhao

AU - Zhang, Yifei

AU - Ke, Wei

AU - Wan, Fang

AU - Ye, Qixiang

PY - 2019/6

Y1 - 2019/6

N2 - We propose an efficient and effective control point extraction algorithm for parametric skeleton generation. The object skeleton pixels are predicted via an hourglass network and partitioned into skeleton branches using Gaussian Mixture Models. For each skeleton branch, a Bezier curve is utilized to generate the control points. The radius of the skeleton is computed by the distance between the border of the object and the Bezier curve. The branches are sorted by the area so that the parametric skeleton representation is unique. For the Parametric SkelNetOn competition, the proposed approach achieves the prediction score of 11793.89, which is in the first place on the performance leader-board.

AB - We propose an efficient and effective control point extraction algorithm for parametric skeleton generation. The object skeleton pixels are predicted via an hourglass network and partitioned into skeleton branches using Gaussian Mixture Models. For each skeleton branch, a Bezier curve is utilized to generate the control points. The radius of the skeleton is computed by the distance between the border of the object and the Bezier curve. The branches are sorted by the area so that the parametric skeleton representation is unique. For the Parametric SkelNetOn competition, the proposed approach achieves the prediction score of 11793.89, which is in the first place on the performance leader-board.

UR - https://www.scopus.com/pages/publications/85081156664

U2 - 10.1109/CVPRW.2019.00153

DO - 10.1109/CVPRW.2019.00153

M3 - 会议稿件

AN - SCOPUS:85081156664

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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EP - 1171

BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019

PB - IEEE Computer Society

T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019

Y2 - 16 June 2019 through 20 June 2019

ER -

Liu C, Luo D, Zhang Y, Ke W, Wan F, Ye Q. Parametric skeleton generation via gaussian mixture models. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society. 2019. p. 1167-1171. 9025510. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW.2019.00153

Parametric skeleton generation via gaussian mixture models

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