State of the Art in Example-Based Texture Synthesis

Sep 10, 2014

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State of the Art in Example-Based Texture Synthesis. Li-Yi Wei Sylvain Lefebvre Vivek Kwatra Greg Turk. Speakers. Li-Yi Wei. Sylvain Lefebvre.

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State of the Art in Example-Based Texture Synthesis

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State of the Art inExample-Based Texture Synthesis Li-Yi Wei Sylvain Lefebvre Vivek Kwatra Greg Turk
Speakers Li-Yi Wei Sylvain Lefebvre

Texture Synthesis

Texture Synthesis. No Texture. Using a sampling of 2D perlin Noise provides smooth hills. Terrain Coloring. Using a 1D texture map based on the altitude can provide many useful mapping. Terrain Coloring. Striped 1D texture map. Terrain Coloring.

678 views • 45 slides

Inverse Texture Synthesis

Inverse Texture Synthesis. Li-Yi Wei 1 Jianwei Han 2 Kun Zhou 1,2 Hujun Bao 2 Baining Guo 1 Harry Shum 1 1 Microsoft 2 Zhejiang University. Old slide from SIGGRAPH 2000. texture synthesis. input (small). output (large).

240 views • 6 slides

Procedural Texture Synthesis

Procedural Texture Synthesis. Patterns from Algorithms. Procedural Synthesis. The basic problem: Worlds are big . To create models and textures for even fairly small worlds takes ages, if you do it by hand. Idea: write down the rules of the world, and a program can create the content.

543 views • 36 slides

Texture Synthesis (Basic)

Texture Synthesis (Basic). Connelly Barnes Computational Photography, Fall 2013. Many slides from James Hays, Alexei Efros. Project 1. Project 1 posted to course website: http:// www.cs.virginia.edu /~connelly/class/2013/cs6501/proj1/. Texture. Texture depicts spatially repeating patterns

540 views • 37 slides

Inverse Texture Synthesis

Inverse Texture Synthesis. Li-Yi Wei 1 Jianwei Han 2 Kun Zhou 1 , 2 Hujun Bao 2 Baining Guo 1 Harry Shum 1 1 Microsoft 2 Zhejiang University. Example-based texture synthesis. For a small input texture produce an arbitrarily large output with similar look

446 views • 29 slides

Competency-based Clinical Supervision: State of the Art

Competency-based Clinical Supervision: State of the Art. Carol Falender, Ph.D. www.cfalender.com. Casebook for Clinical Supervision: A Competency-Based Approach (APA, 2008) Carol A. Falender & Edward P. Shafranske (Eds.) Clinical Supervision: A Competency-Based Approach (APA, 2004)

1.37k views • 94 slides

Texture Synthesis by Patch-Based Sampling

Texture Synthesis by Patch-Based Sampling. Real-Time Texture Synthesis By Patch-Based Sampling , LinLiang, Ce-Liu, Yingqing Xu, Baining Guo, Heung-Yeng Shum, 2001. Presented by: Maryia Kazakevich Dec.3, 2004. Outline. Background to Texture Synthesis Patch-based sampling Algorithm outline

317 views • 13 slides

Overview of Texture Synthesis

Overview of Texture Synthesis. Ganesh Ramanarayanan Cornell Graphics Seminar. ???. small texture. big texture. The Problem. ???. small texture. big texture. A Simple Solution …. small texture. A Simple Solution … Tiling. ???. big texture. A Simple Solution … Tiling.

878 views • 56 slides

MP1: Texture Synthesis

MP1: Texture Synthesis. Advanced Graphics Rendering and Appearance Modeling Chris Lattner March 20, 2003. What I’ve Done. 2D Texture Synthesis: Algorithm from Li-Yi Wei’s Ph.D thesis “Texture Synthesis by Fixed Neighborhood Searching” Single Resolution + Multi-resolution Algorithms

269 views • 8 slides

Texture Optimization for Example-based Synthesis

Texture Optimization for Example-based Synthesis. Vivek Kwatra Irfan Essa Aaron Bobick Nipun Kwatra. Image vs. Texture. Example-based Texture Synthesis. Input Example. Related Work. Local region-growing method -Pixel-based - Patch-based Global optimization-based method

639 views • 42 slides

STATE OF THE ART

STATE OF THE ART. Section 2. Overview We are going to briefly describe some state-of-the-art supercomputers The goal is to evaluate the degree of integration of the three main components, processing nodes, interconnection network and system software

653 views • 42 slides

Texture Synthesis

Texture Synthesis. Texture. Today’s Reading Alexei A. Efros and Thomas K. Leung , “Texture Synthesis by Non-parametric Sampling,” Proc. International Conference on Computer Vision (ICCV), 1999. http://www.cs.berkeley.edu/~efros/research/NPS/efros-iccv99.pdf. Modeling Texture.

513 views • 35 slides

Texture Synthesis on Surfaces

Texture Synthesis on Surfaces. Paper by Greg Turk Presentation by Jon Super. Texture Synthesis on Surfaces. +. Goals of texture placement. Take a 2D texture and wrap it onto a surface Avoid noticeable seams between patches Minimize stretching and distortion

437 views • 30 slides

State of the Art: Model-based Software Engineering

State of the Art: Model-based Software Engineering. The Vision During object design we build an object design model that realizes the use case model and which is the basis for implementation (model-driven design) The Reality

399 views • 28 slides

2D Texture Synthesis

2D Texture Synthesis. Instructor: Yizhou Yu. Texture synthesis. Goal: increase texture resolution yet keep local texture variation. Synthesis by global statistics. Idea: Obtain statistics of input image, match with output image Histograms: normalized graph of intensity frequencies.

450 views • 30 slides

Texture Optimization for Example-based Synthesis

Texture Optimization for Example-based Synthesis. Vivek Kwatra 1,2 Irfan Essa 1 Aaron Bobick 1 Nipun Kwatra 1. 1 GVU Center/College of Computing, Georgia Tech 2 Computer Science Dept., UNC Chapel Hill. Motivation. +. =. Input Texture. Input Flow Field. Output Sequence.

660 views • 52 slides

Texture Optimization for Example-based Synthesis

Texture Optimization for Example-based Synthesis. Vivek Kwatra Irfan Essa Aaron Bobick Nipun Kwatra. GVU Center/College of Computing, Georgia Tech. Objects vs. Textures. Objects Computer Graphics Primitives Described by structure. Objects vs. Textures. Textures Characterized by appearance

483 views • 47 slides

Texture Optimization for Example-based Synthesis

556 views • 52 slides

Texture Synthesis and Transfer

Texture Synthesis and Transfer. William Wedler 15-463 Final Project Fall 2007. Project Description. Synthetic texture from a given sample Extract blocks and fit them together Find blocks that match along their borders Piece together with jagged edges Justification: Markov property

91 views • 7 slides

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