Sciweavers

Share
NIPS
2003

Learning Non-Rigid 3D Shape from 2D Motion

9 years 7 months ago
Learning Non-Rigid 3D Shape from 2D Motion
This paper presents an algorithm for learning the time-varying shape of a non-rigid 3D object from uncalibrated 2D tracking data. We model shape motion as a rigid component (rotation and translation) combined with a non-rigid deformation. Reconstruction is ill-posed if arbitrary deformations are allowed. We constrain the problem by assuming that the object shape at each time instant is drawn from a Gaussian distribution. Based on this assumption, the algorithm simultaneously estimates 3D shape and motion for each time frame, learns the parameters of the Gaussian, and robustly fills-in missing data points. We then extend the algorithm to model temporal smoothness in object shape, thus allowing it to handle severe cases of missing data.
Lorenzo Torresani, Aaron Hertzmann, Christoph Breg
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Lorenzo Torresani, Aaron Hertzmann, Christoph Bregler
Comments (0)
books