Dynamic textures synthesis as nonlinear manifold learning and traversing

Che Bin Liu, Ruei Sung Lin, Narendra Ahuja, Ming Hsuan Yang

Research output: Contribution to conferencePaperpeer-review


We formulate the problem of dynamic texture synthesis as a nonlinear manifold learning and traversing problem. We characterize dynamic textures as the temporal changes in spectral parameters of image sequences. For continuous changes of such parameters, it is commonly assumed that all these parameters lie on or close to a low-dimensional manifold embedded in the original configuration space. For complex dynamic data, the manifolds are usually nonlinear and we propose to use a mixture of linear subspaces to model a nonlinear manifold. These locally linear subspaces are further aligned within a global coordinate system. With the nonlinear manifold being globally parameterized, we overcome motion discontinuity problems encountered in switching linear models and dynamics. We present a nonparametric method to describe the complex dynamics of data sequences on the manifold. We also apply such approach to dynamic spatial parameters such as motion capture data. The experimental results suggest that our approach is able to synthesize smooth, complex dynamic textures and human motions, and has potential applications to other dynamic data synthesis problems.

Original languageEnglish (US)
Number of pages10
StatePublished - 2006
Event2006 17th British Machine Vision Conference, BMVC 2006 - Edinburgh, United Kingdom
Duration: Sep 4 2006Sep 7 2006


Other2006 17th British Machine Vision Conference, BMVC 2006
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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