Dynamic Texture Synthesis via Image Reconstruction

This paper addresses the problem of synthesizing continuous and infinitely varying stream of texture videos by doing operations on finite texture videos. Given an input texture video, such as flame, water, smoke, etc, we can synthesize a longer texture video holding the same texture appearance. Dynamic textures have been modeled as linear dynamic systems (LDS) by unfolding the video frames into column vectors and modeling their dynamic trajectory as time evolves. After the vectors are projected onto a lower dimensional space by Singular Value Decomposition (SVD), dynamic texture synthesis is achieved by driving the system with random noise. However, because of its over-simplified appearance model and under-constrained dynamic model. It is usually hard to synthesize long and visual pleasing texture video sequences. In this paper, we propose a new dynamic texture synthesis framework via creatively fitting the basic LDS with a newly developed patch reconstruction technique to efficiently enhance high quality texture details while maintaining the temporal coherence of the reconstructed texture patches. The patch reconstruction technique is inspired by locally linear embedding (LLE) and based on the assumption that small patches in the low-and high-quality images form manifolds with similar local geometry. The newly synthesized patches are finally stitched together by graph cuts to make up the output texture videos. Experiments on standard dynamic texture databases demonstrate that our method exhibits superior performance on synthesizing dynamic textures.