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Capture of Hair GeometryCapture of Hair Geometryfrom Multiple Imagesfrom Multiple Images
Capture of Hair GeometryCapture of Hair Geometryfrom Multiple Imagesfrom Multiple Images
Sylvain Paris – Hector M. Briceño – François X. Sillion
ARTIS is a team of the GRAVIR - IMAG research lab(UMR 5527 between CNRS, INPG, INRIA and UJF), and a project of INRIA.
MotivationMotivationMotivationMotivation
Digital humans more and more common Movies, games…
Hairstyle is important Characteristic feature
Duplicating real hairstyle
Dusk demo - NVIDIA© 2004 NVIDIA Corporation. Dusk image is © 2004
by NVIDIA Corporation. All rights reserved.
MotivationMotivationMotivationMotivation
User-based duplication of hair Creation from scratch Edition at fine level
Image-based capture Automatic creation Copy of original features
Edition still possible [Kim02]
Our approachOur approachOur approachOur approach
Digital copy of real hairstyle
Only static geometry(animation and appearance as future work)
Dense set of 3D strandsfrom
multiple images
OutlineOutlineOutlineOutline
• Previous work
• Definitions and overview
• Details of the hair capture
• Results
• Conclusions
Previous workPrevious workShape reconstructionShape reconstructionPrevious workPrevious workShape reconstructionShape reconstruction
Computer Vision techniques– Shape from motion, shading, specularities
3D scanners
Difficulties with hair complexity
Only surfaces
Previous workPrevious workLight-field approachLight-field approachPrevious workPrevious workLight-field approachLight-field approach
Matusik et al. 2002
New images from:Different viewpoints + lightsAlpha mattes
Duplication of hairstyle
No 3D strands
Not editable[Matusik02]
Previous workPrevious workEditing packagesEditing packagesPrevious workPrevious workEditing packagesEditing packages
Hadap and Magnenat-Thalmann 2001
Kim et al. 2002
Dedicated tools to help the user
3D strands
Total control
Time-consuming
Duplication very hard
[Hadap01]MIRALab, University of Geneva
[Kim02]
Previous workPrevious workProcedural & Image-basedProcedural & Image-basedPrevious workPrevious workProcedural & Image-basedProcedural & Image-based
Kong et al. 1997
Hair volume from images
Procedural filling
3D strands
Duplication of hair volume
No duplication of hairstyle
New procedure for each hair type[Kong97]
Previous workPrevious workImage-basedImage-basedPrevious workPrevious workImage-basedImage-based
Grabli et al. 2002
Fixed camera, moving light
3D from shading
3D strands
Duplication of hairstyle
Partial reconstruction (holes)
We build upon their approach. [Grabli02]
Captured geometry
Sample input image
1. Dense and reliable 2D data Robust image analysis
2. From 2D to 3D Reflection variation analysis
• Light moves, camera is fixed. Several light sweeps for all hair orientations
3. Complete hairstyle Above process from several viewpoints
Our approachOur approachOur approachOur approach
OutlineOutlineOutlineOutline
Previous work
• Definitions and overview
Details of the hair capture
Results
Conclusions
DefinitionsDefinitionsDefinitionsDefinitions
Fiber
StrandVisible entity
SegmentProject on 1 pixel
Orientation
~1mm
Setup & inputSetup & inputSetup & inputSetup & input
Input summaryInput summaryInput summaryInput summary
We use:
4 viewpoints
2 sweeps per viewpoint
50 to 100 images per sweep
Camera and light positions known
Hair region known (binary mask)
Camera
s
one b
y one
All cam
eras
togeth
er
Main stepsMain stepsMain stepsMain steps
1. Image analysis 2D orientation
2. Highlight analysis 3D orientation
3. Segment chaining 3D strands
Camera
s
one b
y one
All cam
eras
togeth
er
Main stepsMain stepsMain stepsMain steps
1. Image analysis 2D orientation
2. Highlight analysis 3D orientation
3. Segment chaining 3D strands
Measure of 2D orientationMeasure of 2D orientationDifficult pointsDifficult pointsMeasure of 2D orientationMeasure of 2D orientationDifficult pointsDifficult points
Fiber smaller than pixel aliasing
Complex light interaction Scattering, self-shadowing…
Varying image properties
Selectmeasure method
per pixel
Measure of 2D orientationMeasure of 2D orientationUseful informationUseful informationMeasure of 2D orientationMeasure of 2D orientationUseful informationUseful information
Many images available
……
Selectlight positionper pixel
Our approachOur approachOur approachOur approach
Based on oriented filters
Try several options Use the ``best’’
= argmax |K I|
0° 180°
response
Most reliable most discriminantLowest variance
90°
Filter selectionFilter selectionFilter selectionFilter selection
ImplementationImplementationImplementationImplementation
1. Pre-processing: Filter images
2. For each pixel, test:
Filter profiles
Filter parameters
Light positions
Pick option with lowest variance
3. Post-processing: Smooth orientations (bilateral filter)
2 4 8
8
Per pixel selectionPer pixel selectionPer pixel selectionPer pixel selection
4
Canny
2
Gabor
4
2nd Gauss.
2D results2D results2D results2D results
Sobel [Grabli02] Our result
(More results in the paper)
8 filter profiles3 filter parameters
9 light positions
All cam
eras
togeth
er
Camera
s
one b
y one
Main stepsMain stepsMain stepsMain steps
1. Image analysis 2D orientation
2. Highlight analysis 3D orientation
3. Segment chaining 3D strands
Mirror reflectionMirror reflectionComputing segment normalComputing segment normalMirror reflectionMirror reflectionComputing segment normalComputing segment normal
~3° accuracy [Marschner03]
n
For each pixel:Light position?
Practical measurePractical measurePractical measurePractical measure
Orientation from 2 planesOrientation from 2 planesOrientation from 2 planesOrientation from 2 planes
Intersection2 planes
3Dorientation
n
(3D position determined later)
All cam
eras
togeth
er
Camera
s
one b
y one
Main stepsMain stepsMain stepsMain steps
1. Image analysis 2D orientation
2. Highlight analysis 3D orientation
3. Segment chaining 3D strands
Starting point Starting point of a strandof a strandStarting point Starting point of a strandof a strand
Headapproximation
3D ellipsoid
…
Chaining the segmentsChaining the segmentsChaining the segmentsChaining the segments
?
Blending weightsBlending weightsBlending weightsBlending weights
Ending criterionEnding criterionEnding criterionEnding criterion
Strand grows until:
Limit length (user setting)
Out of volume (visual hull)
OutlineOutlineOutlineOutline
Previous work
Definitions and overview
Details of the hair capture
• Results
Conclusions
ResultsResultsResultsResults
Result summaryResult summaryResult summaryResult summary
Similar reflection patterns
Duplication of hairstyle
Curly, wavy and tangled
Blond, auburn and black
Middle length, long and short
ConclusionsConclusionsConclusionsConclusions
General contributions– Dense 2D orientation (filter selection)– 3D from highlights on hair
System– Proof of concept– Sufficient to validate the approach
Capture of a whole head of hair Different hair types
LimitationsLimitationsLimitationsLimitations
• Image-based approach: only visible part Occlusions not handled (curls)
• Head: poor approximation
• Setup: makes the subject moveDuring light sweepBetween viewpoints
Future workFuture workFuture workFuture work
• Better setup and better head approximation
Short term• Data structures for editing and animation• Reflectance
Long term• Hair motion capture• Extended study of filter selection
Thanks…Thanks…Questions ?Questions ?
Thanks…Thanks…Questions ?Questions ?
The authors thank Stéphane Grabli, Steven Marschner, Laure Heïgéas, Stéphane Guy, Marc Lapierre, John Hughes, and Gilles Debunne.
The images in the previous work appear by courtesy by NVIDIA, Tae-Yong Kim,
Wojciech Matusik, Nadia Magnenat-Thalmann, Hiroki Takahashi, and Stéphane Grabli.
Rendering usingdeep shadow mapskindly provided by Florence Bertails.
QuestionsQuestionsQuestionsQuestions
Visual hull Grazing angle
2D validation
Pre-processing Post-processing
Comparisons