Visibility constrained generative model for depth-based 3D facial pose tracking

Lu Sheng, Jianfei Cai, Tat-Jen Cham, Vladimir Pavlovic, King Ngi Ngan

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7 Citations (Scopus)


In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods.

Original languageEnglish
Article number8502935
Pages (from-to)1994-2007
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
Publication statusPublished - 1 Aug 2019
Externally publishedYes


  • 3D facial pose tracking
  • depth
  • generative model
  • mixture of Gaussian models
  • online Bayesian model

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