Graphics, Vision, HCI

Graphics, Vision, HCIGraphics, Vision, HCI

K.P. Chan

Wenping Wang

Li-Yi Wei

Kenneth Wong

Yizhou Yu

Li-Yi WeiLi-Yi Wei

Background

Stanford (95-01), NVIDIA (01-05), MSR (05-11)

Research

Nominal: Graphics, HCI, parallelism

Actual: Computing natural repetitions

(Computer science is about repetitions)

Can work on almost anything + have fun

I tailor projects for individual students (so they also have fun)

Computing natural repetitionsComputing natural repetitions

interactauto

procedural(parametric)

data driven(non-parametric)

parallelrandom

parallelism HCIgraphics

ParallelPoisson

texture synthesisinverse

synthesis

blue noise

motiontexture HDR

edit

elementtexture

revisioncontrol

differentialanalysis

Discrete element textures[Ma et al. SIGGRAPH 2011]Discrete element textures[Ma et al. SIGGRAPH 2011]

exemplar

domain output

synthesis

SIGGRAPHSIGGRAPH

The coolest (& ass kicking) venue in graphics

Each paper can be worth a PhD thesis

(Just in case you don’t know)

HKU has 4 papers in SIGGRAPH 2012 So we are awesome (in addition to have fun)

input

output

input

output

input

output

Yizhou YuYizhou Yu

Background

Berkeley (PhD 2000), UIUC (2000 - 2010)

Research

Graphics, vision, image processing

• Computational Photography

• Computer Animation

• Geometry Processing

• Medical Imaging

• Video Analytics

Deformation transfer for real time cloth animation [SIGGRAPH 2010]Deformation transfer for real time cloth animation [SIGGRAPH 2010]

DeformationTransformer

Motivation• Real-Time Cloth Animation

– Video games, virtual fashion, etc. • The Problem

– Real-time performance on high-resolution models – PDE Integration, Collision resolution.

Final Fantasy XIII Nurien

Overview• Hybrid Approach :

– Simulate low-res cloth on the GPU– Rely on a data-driven model to transform the

low-res simulation into a high-res animation

DeformationTransformer

An Example

High-Res Dress: 27K Triangles, Low-Res Dress: 200 Triangles Frame Rate: 261

Data-Driven Image Color Theme Enhancement [SIGGRAPH Asia 2010]

Photo Reuse: how to edit a photograph to enhance a desired color impression by exploiting prior knowledge extracted from an existing photo collection?

source image nostalgic lively

Waiting for the right season and illumination could be extremely time-consuming!

Our Goal

Image Color Theme Enhancement

Input Image

desolate

lively

Results

Input Images

happy sad

spring in the air peaceful

Wenping WangWenping Wang

Background

Alberta (PhD 1992), Department Head

Research

Computer graphics

Geometry Processing

Computational geometry

Architectural Design

Scientific Visualization

SIGGRAPH 2006

SIGGRAPH 2007

SIGGRAPH 2008

SIGGRAPH 2008

Kwan-Yee Kenneth WongKwan-Yee Kenneth Wong

Background

Cambridge (PhD 2001)

Research

3D modeling

Video surveillance

Image processing

Pattern recognition

…

3D Model ReconstructionRobust recovery of shapes with unknown

topology from the dual space (PAMI 2007)

contour generator

silhouette N

3D Model ReconstructionRobust recovery of shapes with unknown

topology from the dual space (PAMI 2007)

original surface

dual surface

tangent

operation

tangent

operation

original surface

3D Model ReconstructionRobust recovery of shapes with unknown

topology from the dual space (PAMI 2007)

Eye Gaze TrackingReconstruction of display and eyes from a

single image (CVPR 2010)

27

Eye Gaze TrackingReconstruction of display and eyes from a

single image (CVPR 2010)

28

Kwok-Ping ChanKwok-Ping Chan

Background

HKU (PhD 1989)

Research

• To apply various Machine Learning methods on Pattern Recognitions, such as facial expression recognition.

• Study on Cross Domain Learning where the training and the testing domain are not the same.

Facial Expression RecognitionFacial Expression Recognition

Goal: to recognize one of the seven basic facial expressions:

MethodsMethods

• Dynamic Bayesian Network

• Discriminative Hidden Markov Models

• Discriminative Temporal Topic Models

• Given an image sequence of facial expression, we compute the probability of each expression using the above techniques.

Examples: Smile with blinking eyes:Examples: Smile with blinking eyes:

From input, produce output

similar to input

arbitrary size

Key Publication: CVPR 2009