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

Download the digital copy of the doc pdf document

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, pp. 2179-2187.