ML Anthology

Estimating 2D Multi-Hand Poses from Single Depth Images

Le Duan, Minmin Shen, Song Cui, Zhexiao Guo, Oliver Deussen
ECCVW 2018 pp. 257-272
doi:10.1007/978-3-030-11024-6_17 /eccvw/2018/duan2018eccvw-estimating/

Abstract

We present a novel framework based on Pictorial Structure (PS) models to estimate 2D multi-hand poses from depth images. Most existing single-hand pose estimation algorithms are either subject to strong assumptions or depend on a weak detector to detect the human hand. We utilize Mask R-CNN to avoid both aforementioned constraints. The proposed framework allows detection of multi-hand instances and localization of hand joints simultaneously. Our experiments show that our method is superior to existing methods.

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Cite

Text

Duan et al. "Estimating 2D Multi-Hand Poses from Single Depth Images." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11024-6_17

Markdown

[Duan et al. "Estimating 2D Multi-Hand Poses from Single Depth Images." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/duan2018eccvw-estimating/) doi:10.1007/978-3-030-11024-6_17

BibTeX

@inproceedings{duan2018eccvw-estimating,
  title     = {{Estimating 2D Multi-Hand Poses from Single Depth Images}},
  author    = {Duan, Le and Shen, Minmin and Cui, Song and Guo, Zhexiao and Deussen, Oliver},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2018},
  pages     = {257-272},
  doi       = {10.1007/978-3-030-11024-6_17},
  url       = {https://mlanthology.org/eccvw/2018/duan2018eccvw-estimating/}
}