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Style-Content Separation by Anisotropic Part Scales

Kai Xu, Honghua Li, Hao Zhang, Daniel Cohen-OrYueshan Xiong, Zhi-Quan Cheng

Simon Fraser Universtiy

National Univ. of Defense Tech.

Tel-Aviv University

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Background

• Motivation: Enrich a set of 3D models

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Background

• How to create new shapes?

Geometric (content) difference

Part proportion (style) difference

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?

Background

• How to create new shapes?Style transfer

Part proportion style

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Background

• How to create new shapes?Style transfer

Style

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Background

• Style transfer is difficult:– Unsupervised– Correspondence is difficult to compute!

• Geometry• Part proportion

Significant shape variations!

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Background

• To address geometric variations:– Work at part level

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Background

• To address the part proportion variations:– Separate “style” from “content”

Style 1

Style 2

Style 3

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Back to our motivation…

• Fill in the table:

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Style-Content Separation

• Fundamental to human perception

Content Style

Language Words Accents

Text Letters Fonts

Human face Identities Expressions

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Style-Content Separation

• Previous works:

[Tanenbaum and Freeman 2000] Parameterized model

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Style-Content Separation

• Previous works:

“Morphable model”[Blatz and Vetter 1999]Statistical modeling

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Style-Content Separation

• Previous works:

“Style machines”[Brand and Hertzmann 2000]Statistical modeling

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Style-Content Separation

• Previous works:– Prerequisite: data correspondence– Dealt with independently– Correspondence itself is challenging!

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Style-Content Separation

• Our style:– Anisotropic Part Scales

• Our method:– Apply style-content separation in the correspondence stage!

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Algorithm Overview

• Pipeline

Style clustering Co-segmentation Inter-style part correspondence

Contentclassification

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Anisotropic Part Scales Style

• Idea:– Measure style distance between two shapes

Computestyle signature

……

Part OBB Graph of given segme

ntationEuclideanDistance

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Style Distance

• Issues: – Unknown segmentation:

– Unknown correspondence:

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?

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Style Distance

• 2D illustration of style distance

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Style Distance

• 2D illustration of style distance

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Anisotropic Part Scales Style

• Correspondence-free style signature

Binary relations: difference of part scales between adjacent OBBs

Use Laplacian graph spectra:

OBB graph

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Anisotropic Part Scales Style

• Style signature (correspondence free)

Unitary characteristics: anisotropy

OBB graphlinear planar spherical

Encode in graph Laplacian:

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Style Clustering

• Spectral clustering

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Pipeline

Style clustering Co-segmentation Inter-style part correspondence

Contentclassification

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Co-segmentation

• Approach:– “Consistent segmentation of 3D models” [Golovi

nskiy and Funkhouser 2009]– Initial guess: global alignment (ICP)

• We do: within a style cluster– No non-homogeneous part scaling issue!

[Golovinskiy and Funkhouser 2009] Ours

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Pipeline

Style clustering Co-segmentation Inter-style part correspondence

Contentclassification

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Inter-Style Part Correspondence

• Approach: Deform-to-fit– “Deformation driven shape correspondence”

[Zhang et al. 2008]– Possible OBB-to-OBB transformations

1D-to-1D 1D-to-2D 2D-to-2D 2D-to-3D

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Inter-Style Part Correspondence

• Approach: Deform-to-fit

Pruned priority-driven search

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Pipeline

Style clustering Co-segmentation Inter-style part correspondence

Contentclassification

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Content Classification

• Approach:– Light Field Descriptor [Chen et al. 2003]

• We do: part-wise comparison

Part-level LFD Global LFD

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Synthesis by Style Transfer

• OBB: scaling• Underlying geometry: space deformation

content

style

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Results

Hammers

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Results

Goblets

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Results

Humanoid

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Limitations and Future Works

• Requirement on datasets:– Same semantic class– Sufficient variety in style– Initial (over) segmentation needs to be

sufficiently meaningful

• Does not create new content

• Only deals with part anisotropic scales

[Funkhouser et. al. 2004]Defining and analyzing of more shape styles!

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Thank you!Thank you!

감감사합니다사합니다 !!

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