Publication
Simultaneous pose and non-rigid shape with particle dynamics
Conference Article
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Edition
2015
Pages
2179-2187
Doc link
http://dx.doi.org/10.1109/CVPR.2015.7298830
File
Abstract
In this paper, we propose a sequential solution to simultaneously estimate camera pose and non-rigid 3D shape from a monocular video. In contrast to most existing approaches that rely on global representations of the shape, we model the object at a local level, as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency of the estimated shape and camera poses. The resulting approach is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, while it does not require any training data at all. Validation is done in a variety of real video sequences, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our system is shown to perform comparable to competing batch, computationally expensive, methods and shows remarkable improvement with respect to the sequential ones.
Categories
computer vision, optimisation.
Scientific reference
A. Agudo and F. Moreno-Noguer. Simultaneous pose and non-rigid shape with particle dynamics, 2015 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2015, Boston, MA, USA, pp. 2179-2187.
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