Texture Optimization for Example-based Synthesis |
We present a novel technique for texture synthesis using
optimization. We define a Markov Random Field (MRF)-based
similarity metric for measuring the quality of synthesized texture
with respect to a given input sample. This allows us to formulate
the synthesis problem as minimization of an energy function, which
is optimized using an Expectation Maximization (EM)-like
algorithm. In contrast to most example-based techniques that do
region-growing, ours is a joint optimization approach that
progressively refines the entire texture. Additionally, our
approach is ideally suited to allow for controllable synthesis of
textures. Specifically, we demonstrate controllability by
animating image textures using flow fields. We allow for general
two-dimensional flow fields that may dynamically change over time.
Applications of this technique include dynamic texturing of fluid
animations and texture-based flow visualization.
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Related
publication: Poster presented at Symposium on Computational Photography and Video (SCPV'05)
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Results: |