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Cloth

Cloth. Report by LIANG Cheng. Background. Garment. Cloth. Pattern. Fiber. Yarn. Background. Woven. Knit. Application. Analysis Design Simulation. Analysis.

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Cloth

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  1. Cloth Report by LIANG Cheng

  2. Background Garment Cloth Pattern Fiber Yarn

  3. Background Woven Knit

  4. Application • Analysis • Design • Simulation

  5. 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

  6. Design

  7. 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)

  8. Simulation • Cloth • Physical based • Collision detection (video)

  9. DRAPE :Dressing Any Person(SIG12) Peng Guan1; Loretta Reiss1; David A. Hirshberg2; Alexander Weiss1; Michael J. Black1;21Brown University, 2Max Planck Institute for Intelligent Systems

  10. Preprocessing • Training set: • Shape dependent • Pose dependent • SCAPE body models [Anguelov et al. 2005] • Physically Simluation • Use OptiTex Software

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

  12. Define a deformation variations in clothing shape on different people non-rigid pose-dependent deformation rigid rotation applied to clothing part p containing triangle t

  13. Deformation Due to Body Shape Slove Dt Shape Training Set identity matrix identity matrix

  14. Rigid Part Rotation • The SCAPE pose is given by the parameters • Map it to the corresponding cloth part

  15. Deformations Due to Body Pose Slove Qt use second order dynamics model Post Training Set identity matrix

  16. Removing interpenetration • Move the mesh outside the body • Consider four terms • Cloth-body interpenetration • Smooth warping • Damping • Tightness • Minimize the energy function • Iteration

  17. 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.

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