Figure 1. Frame-frame tracked mesh estimation process on the k-th level of the coarse- to-fine framework.
We present the initial focus on a local motion constraint, namely Laplacian Mesh constraint, to improve pairwise optical flow estimation on a typical nonrigid surface i.e. cloth. The Laplacian Mesh constraint is presented as the inherent geometric relation between a pixel and its adjacent neighbours: the movement of connected vertices (pixels) on a deformable surface behaves similarly within a small neighbourhood even when some vertices are occluded. This observation also holds in each pixel within a real-world nonrigid surface. Combining this constraint and a variational optical flow framework, highly accurate correspondence is obtained between an image pair containing nonrigidly deforming objects. The experiments demonstrate the success and outperforms many previous methods on several benchmark datasets.
W. Li, D. Chen, D. Cosker, Matthew Brown Nonrigid Optical Flow and Realworld Ground Truth, Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI'16), 2016. [PDF]
W. Li, D. Cosker, Video Interpolation using Optical flow and Laplacian Smoothness, Neurocomputing 2016. [PDF]
W. Li, D. Cosker, M. Brown, and R. Tang, Optical Flow Estimation using Laplacian Mesh Energy, in Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’13), IEEE, June 2013, pp. 2435–2442. [PDF]