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Personalizing human video pose estimation

Abstract:

We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person’s appearance to improve pose estimation in long videos


We make the following contributions: (i) we show that given a few high-precision pose annotations, e.g. from a generic ConvNet pose estimator, additional annotations can be generated throughout the video using a combination of image-based matching for temporally distant frames, and dense optical flow for temporal...

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Publication status:
In press
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/CVPR.2016.334

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2016-06-05
Acceptance date:
2016-03-01
DOI:
URN:
uuid:046eef2e-1918-4bb7-ac4e-3bda9ee7f90e
Source identifiers:
624530
Local pid:
pubs:624530

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