Video & Capture
SIGGRAPH Asia 2011
1
Modeling and Generating Moving Trees from Video
Chuan Li, Oliver Deussen, Yi-Zhe Song, Phil Willis, Peter HallMedia Technology Research Centre, Unicersity of Konstanz, Centre fro Digital
Entertainment
2
Contribution: improving 3D model and moving
The user outline the tree in an initial video frame
Modeling and Generating Moving Trees from Video
Chuan Li, Oliver Deussen, Yi-Zhe Song, Phil Willis, Peter HallMedia Technology Research Centre, Unicersity of Konstanz, Centre fro Digital
Entertainment
3
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 )
Candid Portrait Selection From Video
Juliet Fiss, Aseem Agarwala, Brian Curless University of Washington, Adobe Systems
4
Select still frames from video
Contribution: the design and execution of a large-scale psychology study
Human subjects collect ratings of video frames
Candid Portrait Selection From Video
Juliet Fiss, Aseem Agarwala, Brian Curless University of Washington, Adobe Systems
5
[System] Face tracking using system by Saragih [2009] Normalized data from human rating and
exception(blink and blur)
Candid Portrait Selection From Video
Juliet Fiss, Aseem Agarwala, Brian Curless University of Washington, Adobe Systems
6
Multiview Face Capture using Polarized Spherical Gradient
IlluminationAbhijeet Gosh, Paul Debevec at et alUSC Institute for Creative Technologies
7
Making facial geometry with high resolutionusing Polarized Spherical Gradient Illumination
Prior limited: position of camera and polarizer Contribution: A new pair of linearly polarized
lightning patterns
Multiview Face Capture using Polarized Spherical Gradient
IlluminationAbhijeet Gosh, Paul Debevec at et alUSC Institute for Creative Technologies
8
The patterns; following lines of latitude and longitude
Multiview Face Capture using Polarized Spherical Gradient
IlluminationAbhijeet Gosh, Paul Debevec at et alUSC Institute for Creative Technologies
9
Results
Video Face ReplacementKevin Dale, Hanspeter Pfister et at al
Harvard Univ, MIT CSAIL, Lantos Technologies, Disney research Zurich
10
A method for replacing facial performances in video
From source video to target video
It does not require ‘manual operation’ and ‘ acquisition hardware’
Video Face ReplacementKevin Dale, Hanspeter Pfister et at al
Harvard Univ, MIT CSAIL, Lantos Technologies, Disney research Zurich
11
Tracking: multilinear method and data (Vlasic [2005])
Retiming: Dynamic Time Warping (Rabiner and Juang [1993])
Blending: to the next page
Video Face ReplacementKevin Dale, Hanspeter Pfister et at al
Harvard Univ, MIT CSAIL, Lantos Technologies, Disney research Zurich
12
Blending: Optimization for seamless face texture
Result
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