Efficient Multi-Constrained Optimization for Example-Based Synthesis

In: The Visual Computer / Proc. Computer Graphics International (CGI 2015) (Juni 2015), 31:6-8(893-904)
 

Abstract

Digital media content comes in a wide variety of modalities and representations. Although they have obvious semantic and structural difference, many of them can be unwrapped into a one-dimensional parameter domain, e.g., time, one spatial dimension. Novel content can then be generated in this parameter domain by computing sequences of elements that are optimal according to an objective to be minimized and in addition satisfy a number of user-defined constraints. Examples for this type of content generation task are audio synthesis, human motion synthesis or architectural texture synthesis. In that work, we present a generalized algorithm for this type of content generation task. We demonstrate the potential of our technique on a selection of content creation tasks, namely the generation of extended animation sequences from motion capture libraries and the example-based synthesis of architectural geometry such as buildings and street blocks.

Stichwörter: Building layouts, Data-driven animation, Example-based synthesis, motion synthesis

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Zusätzliches Material

Bibtex

@ARTICLE{hartmann2015a,
    author = {Hartmann, Stefan and Trunz, Elena and Kr{\"u}ger, Bj{\"o}rn and Klein, Reinhard and Hullin, Matthias B.},
     pages = {893--904},
     title = {Efficient Multi-Constrained Optimization for Example-Based Synthesis},
   journal = {The Visual Computer / Proc. Computer Graphics International (CGI 2015)},
    volume = {31},
    number = {6-8},
      year = {2015},
     month = jun,
  keywords = {Building layouts, Data-driven animation, Example-based synthesis, motion synthesis},
  abstract = {Digital media content comes in a wide variety of modalities and representations. Although they have
              obvious semantic and structural difference, many of them can be unwrapped into a one-dimensional
              parameter domain, e.g., time, one spatial dimension. Novel content can then be generated in this
              parameter domain by computing sequences of elements that are optimal according to an objective to be
              minimized and in addition satisfy a number of user-defined constraints. Examples for this type of
              content generation task are audio synthesis, human motion synthesis or architectural texture
              synthesis. In that work, we present a generalized algorithm for this type of content generation
              task. We demonstrate the potential of our technique on a selection of content creation tasks, namely
              the generation of extended animation sequences from motion capture libraries and the example-based
              synthesis of architectural geometry such as buildings and street blocks.},
       url = {{http://dx.doi.org/10.1007/s00371-015-1114-y}},
       doi = {{10.1007/s00371-015-1114-y}}
}