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Data-Driven Texturing of Human Motions
Abstract
Creating natural looking human animations is a challenging and time-consuming task, even for skilled animators. As manually generating such motions is very costly, tools for accelerating this process are highly desirable, in particular for pre-visualization or animation involving many characters. In this work a novel method for fully automated data-driven texturing of motion data is presented. Based on a database containing a large unorganized collection of motions samples (mocap database) we are able to either: transform a given "raw" motion according to the characteristic features of the motion clips included in the database (style transfer) or even complete partial animation, e.g. by adding the motion of the upper body if only legs have been previously animated (motion completion). By choosing an appropriate database different artistic goals can be achieved such as making a motion more natural or stylized. In contrast to existing approaches like the seminal work by Pullen and Bregler [2002] our method is capable of dealing with arbitrary motion clips without manual steps, i.e. steps involving annotation, segmentation or classification. As indicated by the examples, our technique is able to synthesize smooth transitions between different motion classes if a large mocap database is available. The results are plausible even in case of a very coarse input animation missing root translation.
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Bibtex
@INPROCEEDINGS{krueger-2011-texturing,
author = {Kr{\"u}ger, Bj{\"o}rn and Zinke, Arno and Baumann, Jan and Weber, Andreas},
title = {Data-Driven Texturing of Human Motions},
booktitle = {ACM SIGGRAPH ASIA 2011: Posters},
year = {2011},
month = dec,
location = {Hong Kong, China},
abstract = {Creating natural looking human animations is a challenging and time-consuming task, even for skilled
animators. As manually generating such motions is very costly, tools for accelerating this process
are highly desirable, in particular for pre-visualization or animation involving many characters. In
this work a novel method for fully automated data-driven texturing of motion data is presented.
Based on a database containing a large unorganized collection of motions samples (mocap database) we
are able to either: transform a given "raw" motion according to the characteristic features of the
motion clips included in the database (style transfer) or even complete partial animation, e.g. by
adding the motion of the upper body if only legs have been previously animated (motion completion).
By choosing an appropriate database different artistic goals can be achieved such as making a motion
more natural or stylized.
In contrast to existing approaches like the seminal work by Pullen and Bregler [2002] our method is
capable of dealing with arbitrary motion clips without manual steps, i.e. steps involving
annotation, segmentation or classification. As indicated by the examples, our technique is able to
synthesize smooth transitions between different motion classes if a large mocap database is
available. The results are plausible even in case of a very coarse input animation missing root
translation.}
}