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