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Video & Capture

Video & Capture

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Video & Capture

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  1. Video & Capture SIGGRAPH Asia 2011

  2. Modeling and Generating Moving Trees from VideoChuan Li, Oliver Deussen, Yi-Zhe Song, Phil Willis, Peter HallMedia Technology Research Centre, Unicersity of Konstanz, Centre fro Digital Entertainment • Contribution: improving 3D model and moving • The user outline the tree in an initial video frame

  3. Modeling and Generating Moving Trees from VideoChuan Li, Oliver Deussen, Yi-Zhe Song, Phil Willis, Peter HallMedia Technology Research Centre, Unicersity of Konstanz, Centre fro Digital Entertainment • Video→2D skeleton: using technique (Diener [2006]) • →3D tree model: Copy 2D skeleton and place them • → 3D tree motion: using Bayes`rule(probabilistic Motion modeling)

  4. Candid Portrait Selection From VideoJuliet Fiss, AseemAgarwala, Brian Curless University of Washington, Adobe Systems • Select still frames from video • Contribution: the design and execution of a large-scale psychology study • Human subjects collect ratings of video frames

  5. Candid Portrait Selection From VideoJuliet Fiss, AseemAgarwala, Brian Curless University of Washington, Adobe Systems [System] • Face tracking using system by Saragih [2009] • Normalized data from human rating and exception(blink and blur)

  6. Candid Portrait Selection From VideoJuliet Fiss, AseemAgarwala, Brian Curless University of Washington, Adobe Systems

  7. Multiview Face Capture using Polarized Spherical Gradient IlluminationAbhijeet Gosh, Paul Debevec at et alUSC Institute for Creative Technologies • Making facial geometry with high resolution using Polarized Spherical Gradient Illumination • Prior limited: position of camera and polarizer • Contribution: A new pair of linearly polarized lightning patterns

  8. Multiview Face Capture using Polarized Spherical Gradient IlluminationAbhijeet Gosh, Paul Debevec at et alUSC Institute for Creative Technologies • The patterns; following lines of latitude and longitude

  9. Multiview Face Capture using Polarized Spherical Gradient IlluminationAbhijeet Gosh, Paul Debevec at et alUSC Institute for Creative Technologies • Results

  10. Video Face ReplacementKevin Dale, HanspeterPfister et at al Harvard Univ, MIT CSAIL, Lantos Technologies, Disney research Zurich • A method for replacing facial performances in video • From source video to target video • It does not require ‘manual operation’ and ‘acquisition hardware’

  11. Video Face ReplacementKevin Dale, HanspeterPfister et at al Harvard Univ, MIT CSAIL, Lantos Technologies, Disney research Zurich • Tracking: multilinear method and data (Vlasic [2005]) • Retiming: Dynamic Time Warping (Rabiner and Juang [1993]) • Blending:to the next page

  12. Video Face ReplacementKevin Dale, HanspeterPfister et at al Harvard Univ, MIT CSAIL, Lantos Technologies, Disney research Zurich • Blending: Optimization for seamless face texture Result