How to train your dog: Neural enhancement of quadruped animations

Donal Egan, George Fletcher, Yiguo Qiao, Darren Cosker, Rachel McDonnell

Research output: Chapter in Book/Report/Conference proceedingChapter in a published conference proceeding

Abstract

Creating realistic quadruped animations is challenging. Producing
realistic animations using methods such as key-framing is time consuming and requires much artistic expertise. Alternatively, motion
capture methods have their own challenges (getting the animal into
a studio, attaching motion capture markers, and getting the animal
to put on the desired performance) and the resulting animation
will still most likely require cleaning up. It would be useful if an
animator could provide an initial rough animation and in return be
given a corresponding high quality realistic one. To this end, we
present a deep-learning approach for the automatic enhancement of
quadruped animations. Given an initial animation, possibly lacking
the subtle details of true quadruped motion and/or containing small
errors, our results show that it is possible for a neural network to learn how to add these subtleties and correct errors to produce an
enhanced animation while preserving the semantics and context
of the initial animation. Our work also has potential uses in other
applications, for example, its ability to be used in real-time means
it could form part of a quadruped embodiment system.
Original languageEnglish
Title of host publicationProceedings MIG 2021 - 14th ACM SIGGRAPH Conference on Motion, Interaction and Games
Subtitle of host publication14th ACM SIGGRAPH Conference on Motion, Interaction, and Games
EditorsStephen N. Spencer
Place of PublicationU. S. A.
PublisherAssociation for Computing Machinery
Pages1-7
Number of pages7
ISBN (Electronic)9781450391313
ISBN (Print)9781450391313
DOIs
Publication statusPublished - 10 Nov 2021
Event14th ACM SIGGRAPH Conference on Motion, Interaction and Games -
Duration: 10 Nov 202112 Nov 2021
https://mig2021.inria.fr/

Publication series

NameProceedings - MIG 2021: 14th ACM SIGGRAPH Conference on Motion, Interaction, and Games

Conference

Conference14th ACM SIGGRAPH Conference on Motion, Interaction and Games
Abbreviated titleMIG'21
Period10/11/2112/11/21
Internet address

Keywords

  • Deep-Learning
  • Motion Capture
  • Quadruped Animation

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction
  • Education

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