Top-down model fitting for hand pose recovery in sequences of depth images

Meysam Madadi, Sergio Escalera, Alex Carruesco, Carlos Andujar, Xavier Baró, Jordi Gonzàlez

Research output: Contribution to journalArticleResearch

7 Citations (Scopus)


© 2018 Elsevier B.V. State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.
Original languageEnglish
Pages (from-to)63-75
JournalImage and Vision Computing
Publication statusPublished - 1 Nov 2018


  • Depth image
  • Hand pose recovery
  • Hand segmentation
  • Shape description
  • Temporal modeling


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