Publication
Recovering pose and 3D deformable shape from multi-instance image ensembles
Conference Article
Conference
Asian Conference on Computer Vision (ACCV)
Edition
13th
Pages
291-307
Doc link
http://dx.doi.org/10.1007/978-3-319-54190-7_18
File
Abstract
In recent years, there has been a growing interest on tackling the Non-Rigid Structure from Motion problem (NRSfM), where the shape of a deformable object and the pose of a moving camera are simultaneously estimated from a monocular video sequence. Existing solutions are limited to single objects and continuous, smoothly changing sequences. In this paper we extend NRSfM to a multi-instance domain, in which the images do not need to have temporal consistency, allowing for instance, to jointly reconstruct the face of multiple persons from an unordered list of images. For this purpose, we present a new formulation of the problem based on a dual low-rank shape representation, that simultaneously captures the between- and within-individual deformations. The parameters of this model are learned using a variant of the probabilistic linear discriminant analysis that requires consecutive batches of expectation and maximization steps. The resulting approach estimates 3D deformable shape and pose of multiple instances from only 2D point observations on a collection images, without requiring pre-trained 3D data, and is shown to be robust to noisy measurements and missing points. We provide quantitative and qualitative evaluation on both synthetic and real data, and show consistent benefits compared to current state of the art.
Categories
optimisation.
Author keywords
computer vision
Scientific reference
A. Agudo and F. Moreno-Noguer. Recovering pose and 3D deformable shape from multi-instance image ensembles, 13th Asian Conference on Computer Vision, 2016, Taipei, Taiwan, in Computer Vision – ACCV 2016, Vol 10114 of Lecture Notes in Computer Science, pp. 291-307, 2017, Springer.
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