More details on presentations • Aim to speak for ~50 min (after 15 min review, leaving 10 min for discussions) • Try to plan discussion topics • It’s fine to “steal” slides from the Web, but be sure to acknowledge sources • Include SIGGRAPH videos, demos (if any) • Send me your slides afterwards for including on the class webpage • Feel free to include related papers and background material in the presentation, or make a presentation based on two (or more) closely related papers • Actually, this can make your life easier! • Feel free to propose papers not on the list • Email me four presentation preferences and any scheduling constraints by the end of Sunday • Come and talk to me about any doubts or questions
More details on presentations. Aim to speak for ~50 min (after 15 min review, leaving 10 min for discussions) Try to plan discussion topics It’s fine to “steal” slides from the Web, but be sure to acknowledge sources Include SIGGRAPH videos, demos (if any) - PowerPoint PPT Presentation
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More details on presentations• Aim to speak for ~50 min (after 15 min review,
leaving 10 min for discussions)• Try to plan discussion topics
• It’s fine to “steal” slides from the Web, but be sure to acknowledge sources• Include SIGGRAPH videos, demos (if any)
• Send me your slides afterwards for including on the class webpage
• Feel free to include related papers and background material in the presentation, or make a presentation based on two (or more) closely related papers• Actually, this can make your life easier!
• Feel free to propose papers not on the list• Email me four presentation preferences and any
scheduling constraints by the end of Sunday• Come and talk to me about any doubts or questions
Texture Synthesis
Most slides from A. Efros
Last Time: Beauty in Complexity
Today: Texture
• Texture is “stuff” (as opposed to “things”)
• Characterized by spatially repeating patterns
• Texture lacks the full range of complexity of photographic imagery, but makes a good starting point for study of image-based techniques
radishes rocks yogurt
Texture Synthesis• Goal of Texture Synthesis: create new samples of
a given texture• Many applications: virtual environments, hole-
filling, texturing surfaces
The Challenge
• Need to model the whole spectrum: from repeated to stochastic texture
repeated
stochastic
Both?
Statistical modeling of texture• Assume stochastic model of texture (Markov
Random Field)• Stationarity: the stochastic model is the same
regardless of position
stationary texture non-stationary texture
Statistical modeling of texture• Assume stochastic model of texture (Markov
Random Field)• Stationarity: the stochastic model is the same
regardless of position• Markov property:
p(pixel | rest of image) = p(pixel | neighborhood)
?
Motivation from Language• Shannon (1948) proposed a way to generate
English-looking text using N-grams– Assume a Markov model– Use a large text to compute probability distributions
of each letter given N–1 previous letters – Starting from a seed repeatedly sample the
conditional probabilities to generate new letters– One can use whole words instead of letters too
• For textures with large-scale structures, use a Gaussian pyramid to reduce required neighborhood size– Low-resolution image is
synthesized first
– For synthesis at a given pyramid level, the neighborhood consists of already generated pixels at this level plus all neighboring pixels at the lower level
Li-Yi Wei and Marc Levoy, "Fast Texture Synthesis using Tree-structured Vector Quantization,"