A Groupwise Multilinear Correspondence Optimization for 3D Faces - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

A Groupwise Multilinear Correspondence Optimization for 3D Faces


Multilinear face models are widely used to model the space of human faces with expressions. For databases of 3D human faces of different identities performing multiple expressions, these statistical shape models decouple identity and expression variations. To compute a high-quality multilinear face model, the quality of the registration of the database of 3D face scans used for training is essential. Meanwhile, a multilinear face model can be used as an effective prior to register 3D face scans, which are typically noisy and incomplete. Inspired by the minimum description length approach, we propose the first method to jointly optimize a multilinear model and the registration of the 3D scans used for training. Given an initial registration, our approach fully automatically improves the registration by optimizing an objective function that measures the compactness of the multilinear model, resulting in a sparse model. We choose a continuous representation for each face shape that allows to use a quasi-Newton method in parameter space for optimization. We show that our approach is computationally significantly more efficient and leads to correspondences of higher quality than existing methods based on linear statistical models. This allows us to evaluate our approach on large standard 3D face databases and in the presence of noisy initializations.
Fichier principal
Vignette du fichier
multilinear_correspondence_optimization_final.pdf (3.92 Mo) Télécharger le fichier
Supplemental.pdf (211.32 Ko) Télécharger le fichier
SupplementaryVideo.avi (13.66 Mo) Télécharger le fichier
multilinear-mdl.zip (36.23 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
Format : Video
Origin : Files produced by the author(s)
Comment : Data containing the trained models to allow reproducing the results of the paper.

Dates and versions

hal-01205460 , version 1 (25-09-2015)



Timo Bolkart, Stefanie Wuhrer. A Groupwise Multilinear Correspondence Optimization for 3D Faces. IEEE International Conference on Computer Vision (ICCV), Dec 2015, Santiago, Chile. pp.3604-3612, ⟨10.1109/ICCV.2015.411⟩. ⟨hal-01205460⟩
353 View
1190 Download



Gmail Facebook X LinkedIn More