Cascaded hand pose regression
Refereed conference paper presented and published in conference proceedings


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AbstractWe extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects. Our first contribution is 3D pose-indexed features that generalize the previous 2D parameterized features and achieve better invariance to 3D transformations. Our second contribution is a principled hierarchical regression that is adapted to the articulated object structure. It is therefore more accurate and faster. Comprehensive experiments verify the state-of-the-art accuracy and efficiency of the proposed approach on the challenging 3D hand pose estimation problem, on a public dataset and our new dataset.
All Author(s) ListSun X., Wei Y., Liang S., Tang X., Sun J.
Name of ConferenceIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Start Date of Conference07/06/2015
End Date of Conference12/06/2015
Place of ConferenceBoston
Country/Region of ConferenceUnited States of America
Year2015
Month10
Day14
Volume Number07-12-June-2015
Pages824 - 832
ISBN9781467369640
ISSN1063-6919
LanguagesEnglish-United Kingdom

Last updated on 2020-16-07 at 03:41