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Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection

Emre Dogan 1 Gonen Eren 2 Christian Wolf 1 Eric Lombardi 1 Atilla Baskurt 1 
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We propose a new method for human pose estimation which leverages information from multiple views to impose a strong prior on articulated pose. The novelty of the method concerns the types of coherence modelled. Consistency is maximised over the different views through different terms modelling classical geometric information (coherence of the resulting poses) as well as appearance information which is modelled as latent variables in the global energy function. Moreover, adequacy of each view is assessed and their contributions are adjusted accordingly. Experiments on the HumanEva and UMPM datasets show that the proposed method significantly decreases the estimation error compared to single-view results.
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Preprints, Working Papers, ...
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Contributor : Christian Wolf Connect in order to contact the contributor
Submitted on : Tuesday, September 26, 2017 - 2:12:16 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM

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  • HAL Id : hal-01593547, version 1
  • ARXIV : 1709.08527


Emre Dogan, Gonen Eren, Christian Wolf, Eric Lombardi, Atilla Baskurt. Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection. 2017. ⟨hal-01593547⟩



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