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PhotoCloth

PhotoCloth: A framework to synthesize real-time photorealistic cloth animation from video input.

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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Project "PhotoCloth" data sheet

The following table provides information about the project.

Coordinator
UNIVERSIDAD REY JUAN CARLOS 

Organization address
address: CALLE TULIPAN
city: MOSTOLES
postcode: 28933
website: http://www.urjc.es

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website http://dancasas.github.io/projects/photocloth
 Total cost 170˙121 €
 EC max contribution 170˙121 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-10-01   to  2018-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSIDAD REY JUAN CARLOS ES (MOSTOLES) coordinator 170˙121.00

Map

 Project objective

Computer Graphics is the area of computer science that studies methods for digitally synthesizing and animating visual content. Among all the potential contexts where computer graphics techniques can be used, cloth animation is a particularly interesting case since, in the real world, clothing is far more than just the physical objects that we wear; clothing is a key element to show someone’s expressiveness and motion, it even defines his or her identity.

However, cloth animation is a complex and extremely high-dimensional problem. To digitally synthesize cloth animation, a large number of properties that affect the way cloth behaves need to be estimated: textures, deformations, collisions, materials, illumination, etc.

Current approaches for cloth animation tried to overcome this challenging problem following two main trends: image-based methods use captured data to construct a low-dimensional model to digitally synthesize new animations, however they can only sample a small portion of the high-dimensional space of cloth and poses; the physics-based methods aim to simulate cloth only using mathematical equations that express physics laws, however, they are computationally expensive and have trouble replicating real-world behavior.

This fellowship will investigate a new model for cloth simulation that combines a physical-based method with image-based infomation to generate real-time believable cloth animation. The new model will use a multi-scale framework to handle the dynamic geometry and appearance at different levels of detail. The most salient dynamic geometric properties of the animation will be handled by a low-resolution representation of the cloth using a physics-based model, which reduces the high-dimensionality of the pose space to a lower-dimensional subspace. Mid- and fine-scale details such as shading, wrinkles and appearance will be incorporated by an image-based approach, using the input imagery to learn to predict those properties.

 Publications

year authors and title journal last update
List of publications.
2018 Dan Casas, Miguel A. Otaduy
Learning Nonlinear Soft-Tissue Dynamics for Interactive Avatars
published pages: 1-15, ISSN: 2577-6193, DOI: 10.1145/3203187
Proceedings of the ACM on Computer Graphics and Interactive Techniques 1/1 2019-06-13
2017 Dushyant Mehta, Srinath Sridhar, Oleksandr Sotnychenko, Helge Rhodin, Mohammad Shafiei, Hans-Peter Seidel, Weipeng Xu, Dan Casas, Christian Theobalt
VNect
published pages: 1-14, ISSN: 0730-0301, DOI: 10.1145/3072959.3073596
ACM Transactions on Graphics 36/4 2019-06-13
2018 Mueller, Franziska; Bernard, Florian; Sotnychenko, Oleksandr; Mehta, Dushyant; Sridhar, Srinath; Casas, Dan; Theobalt, Christian
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
published pages: , ISSN: , DOI:
IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019-06-13
2009 Raquel Vidaurre Dan Casas Elena Garces Jorge Lopez-Moreno
BRDF Estimation of Complex Materials with Nested Learning
published pages: , ISSN: , DOI:
IEEE Winter Conference on Applications of Computer Vision (WACV) 2019-05-10

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The information about "PHOTOCLOTH" are provided by the European Opendata Portal: CORDIS opendata.

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