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Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

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arxiv 1406.2984 v2 pith:2NAYMFIG submitted 2014-06-11 cs.CV

Jonathan Tompson , Arjun Jain , Yann LeCun , Christoph Bregler This is my paper · ORCID
classification cs.CV
keywords architecturejointconvolutionalestimationhumanmodelnetworkpose
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images. The architecture can exploit structural domain constraints such as geometric relationships between body joint locations. We show that joint training of these two model paradigms improves performance and allows us to significantly outperform existing state-of-the-art techniques.

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  1. Joint angle based learning to refine kinematic human pose estimation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Joint angle refinement (JAR), trained on Fourier-synthesized angle sequences and applied with a BiGRU-Attention network, smooths keypoint trajectories and corrects outliers in human pose estimates, outperforming Smoot...

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