Depth-based hand pose estimation: data, methods, and challenges
Hand pose estimation has matured rapidly in recent
years. The introduction of commodity depth sensors and
a multitude of practical applications have spurred new advances.
We provide an extensive analysis of the state-of-theart,
focusing on hand pose estimation from a single depth
frame. To do so, we have implemented a considerable number
of systems, and will release all software and evaluation
code. We summarize important conclusions here: (1) Pose
estimation appears roughly solved for scenes with isolated
hands. However, methods still struggle to analyze cluttered
scenes where hands may be interacting with nearby objects
and surfaces. To spur further progress we introduce
a challenging new dataset with diverse, cluttered scenes.
(2) Many methods evaluate themselves with disparate criteria,
making comparisons difficult. We define a consistent
evaluation criteria, rigorously motivated by human experiments.
(3) We introduce a simple nearest-neighbor baseline
that outperforms most existing systems. This implies that
most systems do not generalize beyond their training sets.
This also reinforces the under-appreciated point that training
data is as important as the model itself. We conclude
with directions for future progress.
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Text Reference
James Steven Supan\vc i\vc III, Grégory Rogez, Yi Yang, Jamie Shotton, and Deva Ramanan. Depth-based hand pose estimation: data, methods, and challenges. In IEEE International Conference on Computer Vision. 2015.BibTeX Reference
@INPROCEEDINGS{SupancicRYSR_ICCV_2015,author = "Supan{\vc}i{\vc} III, James Steven and Rogez, Gr{\'e}gory and Yang, Yi and Shotton, Jamie and Ramanan, Deva",
booktitle = "IEEE International Conference on Computer Vision",
title = "Depth-based hand pose estimation: data, methods, and challenges",
year = "2015"
}