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SpaTe SIGNED

Spatio-Temporal Methods for Data-driven Computer Animation and Simulation

Total Cost €

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EC-Contrib. €

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Partnership

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Project "SpaTe" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITAET MUENCHEN 

Organization address
address: Arcisstrasse 21
city: MUENCHEN
postcode: 80333
website: www.tu-muenchen.de

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 Germany [DE]
 Total cost 1˙998˙750 €
 EC max contribution 1˙998˙750 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-COG
 Funding Scheme ERC-COG
 Starting year 2020
 Duration (year-month-day) from 2020-09-01   to  2025-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITAET MUENCHEN DE (MUENCHEN) coordinator 1˙998˙750.00

Map

 Project objective

Numerical simulations are of tremendous importance for a wide range of scientific disciplines and commercial enterprises. For the majority of these natural phenomena, we not only need to consider the three spatial dimensions, but additionally we need to resolve how these phenomena develop over time. Thus, most natural simulations inherently need to resolve four dimensional functions, and most effects at human scales require fine discretizations along all four axes. As a consequence, these functions require large amounts of resources to compute and store. This problem becomes even more pronounced with the advent of data-driven techniques and machine learning. The learning algorithms effectively add additional dimensions, and the complexity and dimensionality of the corresponding functions explains the current lack of data-driven algorithms for space-time functions despite their enormous potential. Within this research project I plan to address the fundamental difficulties that arise in this setting: I will develop novel algorithms to infer spatio-temporal functions, and to construct efficient representations to tame their complexity and high dimensionality. This project combines numerical simulations with computer vision, and machine learning, and has the potential to radically change the way we work with physical simulations. Not only will it break new ground for fast and controllable VFX animations, but it will additionally facilitate the development of new ways to capture physical effects, in conjunction with algorithms to make physical predictions based on observations. Ultimately, this direction will allow us to better understand the physical world around us. It will help us to analyze sparse and ambiguous measurements such as videos and 3D scans automatically and reliably, with a vast range of practical applications from social-media apps to autonomous vehicles.

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

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