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. 1

In this paper, we aim at modeling video sequences that exhibit temporal appearance variation. The dynamic texture model proposed in [6] is effective to model simple dynamic scenes. However, because of its over-simplified appearance model and under-constrained dynamics model, the visual quality of its synthesized video sequences is often not satisfactory. This leads to our new model. We parameterize the nonlinear image manifold using mixtures of probabilistic principal component analyzers. We then align coefficients from different mixture components in a global coordinate system, and model the image dynamics in the global coordinate using an autoregressive process. The experimental results show that our method is capable of capturing complex temporal appearance variation and offers improved synthesis results over previous works.

Most existing video retrieval systems use low-level visual features such as color histogram, shape, texture, or motion. In this paper, we explore the use of higher-level motion representation for video retrieval of dynamic objects. We use three motion representations, which together can retrieve a large variety of motion patterns. Our approach works on top of a tracking unit and assumes that each dynamic object has been tracked and circumscribed in a minimal bounding box in each video frame. We represent the motion attributes of each object in terms of changes in the image context of its circumscribing box. The changes are described via motion templates [4], self-similarity plots [3], and image dynamics [9]. Initially, defined criteria of the retrieval process are interactively refined using relevance feedback from the user. Experimental results demonstrate the use of the proposed motion models in retrieving objects undergoing complex motion.