Navy-Marine Corps Amphibious and Maritime Prepositioning ...
3D Clothing: Garment Prepositioning & Human Body...
Transcript of 3D Clothing: Garment Prepositioning & Human Body...
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Zhang Yuzhe
SCE & IMI
Supervisor: Assoc Prof Zheng Jianmin
Co-Supervisor: Prof Nadia M. Thalmann
2011-11-22
3D Clothing:
Garment Prepositioning & Human Body Adaptation
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Outline
• Introduction
• Garment Prepositioning
• Anthropometry based Body Adaptation
• Future work
• Q & A
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Introduction
• Project – 3D clothing
• Motivation – Digital fashion applications
• Design, sew and simulate 2D patterns of garment on mannequin
• Try to make the whole process more intelligent and automatic
– Two main elements in digital fashion
• Human body model
• Patterns & Garment
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Garment Prepositioning
• Problem:
– Input: 2D patterns, 3D body model
– Output: 3D garment mesh on body model
– Good prepositioning
• decrease computation of simulation
– Bad prepositioning
• lead to failure of simulation
Failure of Simulation
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Garment Prepositioning
• Tasks involved
– Connectivity Task
• Pattern assembly
– Geometry Task
• 3D placement
• Triangulation
stitching
Placement
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Garment Prepositioning
• State-of-the-art
– Manual approach
• Fashionizer
– Semi-automatic approach
• “Automatic pre-positioning of virtual clothing”, C. Grob, SCCG '03
• “ClothAssembler: a CAD module for feature-based garment pattern
assembler”, M. Fontana, J. Computers and Graphics, 06
• “From Interactive Positioning to Automatic Try-On of Garments”, T.
Thanh, MIRAGE '09
Body Segment
Flatten Bounding Surface
Arranging Patterns
Figurines Generic
Silhouette Real
Silhouette
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Garment Prepositioning
• Our Approach
– Basic ideas
• Define our own format for connectivity
• Use template for reference
• Preposition-deformation for geometry
– Workflow
2D
patterns
Body
template
2D Editor
Own
format
3D
Body
Map to
template
Topology
stitching Preposition
3D
garment
Correspond
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Garment Prepositioning
• Data Format
– Geometrical Information
• 2D Contour
• Mesh for 2D patterns
• Reference point and normal
– Topological Information
• Seam line
– Semantic Information
• Markers (link with skeleton)
• Other knowledge (Front/Back/Fold, Layer, Accessory )
Semantic info
Topological info
Geometrical info
Embedded geometrical & topological Info
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Garment Prepositioning
• Map patterns to template body
– Preprocess
• Fold and turn over patterns if necessary
• Normalize patterns of garment to origin
– Semantic based matching
• Heuristic rules based on connectivity among patterns for basic
garment types
• Semantic knowledge embedded in template
Preprocess
Map onto template
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Garment Prepositioning
• Correspond Input body to template
– Anthropometry extraction
– Skeleton and measurement matching
match
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Garment Prepositioning
• Preposition on input body
– Deformation according to corresponding skeleton and measurement
– Minor transformation so that seams do not intersect with body
• Topology stitching
– Assembly patterns into the initial 3D mesh of garment by stitching
seams
Deformation Stitching
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Garment Prepositioning
• Example (Own format Load body Preposition Simulation)
- Template body
- Customized body with different size & pose
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Anthropometry based Body Adaptation
• Problem
– Model and customize an animated human body model
– Input: parameter, template body (with skeleton and skinning info)
– Output: adapted body
• Motivation
– Fast and easy to generate a group of human body model
– Anthropometry based (parameter linked with cloth measurement)
Parameter
control
Animate
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Anthropometry based Body Adaptation
• Tasks involved
– Anthropometric measurements modeling
• 24 measurements
– Girth: waist girth, hip girth
– Length: thigh length, arm length
– Non-geometric: weight
– Analysis of anthropometric measurements
• Limited input parameters
• Model the correlation among measurements
– Deformation under the control of measurements
– Animate model
• Skeleton & skinning information
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• State-of-the-art
– Creative Approach
• “An example-based approach to human body manipulation “<2004>
• “Customizing & populating animated digital mannequins for real-
time application”<2010>
• …
– Disadvantages
• Treat the parameters independently
• Do not capture the detailed shape variations
Anthropometry based Body Adaptation
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• Our approach
– Basic ideas
• Predict full set of parameters from partial ones by analyzing training data
• Customize mesh and skeleton from full set anthropometric measurements
• Interactive modification by detail shape editing
– Workflow
Anthropometry based Body Adaptation
Input 1 Full set of
anthropometric
measurements
Girth &
Length
Deformation
Detail Shape
Editing
Analyze
Deformation
Template
with full
setup
Customized
Animated
model
Animate Motion data
Partial
measure-
ments
Input 2
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Anthropometry based Body Adaptation
• Anthropometry analysis
– Predict the full set measurements w based on correlation
coefficient analysis
Partial
input
α
Training
data
Correlation
Coefficient
Analysis
wnormal
wcurrent Local
Modify wpersonal
Weighted
Combine Full set
w
Input Output
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c
Pj
Pj+1 P’j
P’j+1
Anthropometry based Body Adaptation
• Anthropometry based deformation
– Girth deformation
• Scale feature points on girth
• Laplacian reconstruction with feature constraints
Waist Girth=59.31cm Waist Girth=71.31cm
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Anthropometry based Body Adaptation
• Anthropometry based deformation
– Length deformation
• Linear scale skeleton
• Scale vertices in related regions
• Translate related girth measurement
• Propagate translation to its descendant nodes
∆l
Thigh Length=39.59cm Thigh Length=45.59cm
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Anthropometry based Body Adaptation
• Detail shape editing
– Set intersection plane, select target curve to be deformed
– Approximate target curve by B-Spline curve
– Deform curve by moving control points
– Deformation by Laplacian reconstruction
with constraints
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Anthropometry based Body Adaptation
• Experiment results
– Evaluation of anthropometry analysis
– Comparison with independent
linear scale method
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Anthropometry based Body Adaptation
• More example
– Body adaptation with limited anthropometric measurements
– Animation
• Used in garment simulation example
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Future work
• Automatic patterns assembly and semantic representation
– Learning the semantic information based on shape analysis
– Automatic assembly patterns based on puzzle matching
• Automatic anthropometry extraction of input body
– Extract skeleton and measurements of individual body
– Customized template into individual body automatically according
to extracted knowledge
• Garment resize and flatten
– Resize 3D garment according to the measurements of body model
– Resize 2D patterns by 3D garment flatten
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Thank you !
Q & A