Cloth

Report by LIANG Cheng

Background

Cloth Garment

Pattern

YarnFiber

Background

Woven

Knit

Application

• Analysis• Design• Simulation

Analysis

• Mirjalili S. A., Ekhtiyari E. Wrinkle Assessment of Fabric Using Image Processing. FIBRES & TEXTILES in Eastern Europe 2010, Vol. 18, No. 5 (82) pp. 60-63

Design

Simulation

• Material• Neeharika Adabala, Nadia Magnenat-

Thalmann and Guangzheng Fei. Visualization of woven cloth. Eurographics Symposium on Rendering 2003.

• Jiaping Wang, Shuang Zhao, Xin Tong, John Snyder, and Baining Guo. 2008. Modeling anisotropic surface reflectance with example-based microfacet synthesis. ACM Trans. Graph.27, 3, Article 41 (August 2008)

Simulation

• Cloth– Physical based• Collision detection (video)

DRAPE :Dressing Any Person(SIG12) Peng Guan1; Loretta Reiss1; David A. Hirshberg2; Alexander Weiss1; Michael J. Black1;2

1Brown University, 2Max Planck Institute for Intelligent Systems

Preprocessing

• Training set:– Shape dependent– Pose dependent– SCAPE body models

[Anguelov et al. 2005]

• Physically Simluation– Use OptiTex Software

Framework

• Shape deformation using shape training set• Changing pose using rigid rotation• Add wrinkles using pose training set• Remove cloth interpenetration

Define a deformationvariations in clothing shape on different people

rigid rotation applied to clothing part p containing triangle t

non-rigid pose-dependent deformation

Deformation Due to Body Shape

Slove Dt

Shape Training Set

identity matrix

identity matrix

Rigid Part Rotation

• The SCAPE pose is given by the parameters• Map it to the corresponding cloth part

Deformations Due to Body Pose

Slove Qtuse second order dynamics model

Post Training Set

identity matrix

Removing interpenetration

• Move the mesh outside the body• Consider four terms– Cloth-body interpenetration– Smooth warping– Damping– Tightness

• Minimize the energy function– Iteration

Inspiration

• Separate the deformation into three steps• Use some training set to generation the

deformation

This paper proposes supervised learning of garment parameters used for dressing any input human model in any pose, with highly detailed wrinkles. It decouples the learning of body shape, pose, and detailed wrinkles. The training data is obtained from an interactive dressing software.