Exploiting spatial-temporal relationships for 3D pose estimation via graph convolutional networks

Yujun Cai, Liuhao Ge, Jun Liu, Jianfei Cai, Tat-Jen Cham, Junsong Yuan, Nadia Magnenat Thalmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)

Abstract

Despite great progress in 3D pose estimation from single-view images or videos, it remains a challenging task due to the substantial depth ambiguity and severe self-
occlusions. Motivated by the effectiveness of incorporating spatial dependencies and temporal consistencies to alleviate these issues, we propose a novel graph-based method
to tackle the problem of 3D human body and 3D hand pose estimation from a short sequence of 2D joint detections. Particularly, domain knowledge about the human hand (body) configurations is explicitly incorporated into the graph convolutional operations to meet the specific demand of the 3D pose estimation. Furthermore, we introduce a local-to-global network architecture, which is capable of learning multi-scale features for the graph-based representations. We evaluate the proposed method on challenging benchmark datasets for both 3D hand pose estimation and 3D body pose estimation. Experimental results show that our method achieves state-of-the-art performance on both
tasks.
Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Computer Vision, ICCV 2019
EditorsIn So Kweon, Nikos Paragios, Ming-Hsuan Yang, Svetlana Lazebnik
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2272-2281
Number of pages10
ISBN (Electronic)9781728148038
ISBN (Print)9781728148045
DOIs
Publication statusPublished - 2019
EventIEEE International Conference on Computer Vision 2019 - Seoul, Korea, Republic of (South)
Duration: 27 Oct 20192 Nov 2019
Conference number: 17th
http://iccv2019.thecvf.com/

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2019-October
ISSN (Print)1550-5499
ISSN (Electronic)2380-7504

Conference

ConferenceIEEE International Conference on Computer Vision 2019
Abbreviated titleICCV 2019
CountryKorea, Republic of (South)
CitySeoul
Period27/10/192/11/19
Internet address

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