Tensor-Based Non-Rigid Structure from Motion

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


In this work we present a method that combines tensor-based face modelling and analysis and non-rigid structure-from-motion (NRSFM). The core idea is to see that the conventional tensor formulation for the face structure and expression analysis can be utilised while the structure component can be directly analysed as the non-rigid structure-from-motion problem. To the NRSFM problem part we further present a novel prior-free approach that factorises the 2D input shapes into affine projection matrices, rank-one 3D affine basis shapes, and the basis shape coefficients. The linear combination of the basis shapes thus yields the recovered 3D shapes upto an affine transformation.
In contrast to most works in literature, no calibration information of the cameras or structure prior is required.
Experiments on challenging face datasets show that our method, with and without the metric upgrade, is accurate and fast when compared to the state-of-the-art and is well suitable for dense reconstruction and face editing.
Original languageEnglish
Title of host publicationIEEE Winter Conference on Applications of Computer Vision (WACV) 2022
Publication dateJan 2022
Publication statusPublished - Jan 2022
Event IEEE/CVF Winter Conference on Applications of Computer Vision 2022 - Hawaii, United States
Duration: 4 Jan 20228 Jan 2022


Conference IEEE/CVF Winter Conference on Applications of Computer Vision 2022
Country/TerritoryUnited States
Internet address


  • tensor model
  • non-rigid structure-from-motion


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