Going beyond Free Viewpoint: Creating Animatable Volumetric Video of
Human Performances
- URL: http://arxiv.org/abs/2009.00922v1
- Date: Wed, 2 Sep 2020 09:46:12 GMT
- Title: Going beyond Free Viewpoint: Creating Animatable Volumetric Video of
Human Performances
- Authors: Anna Hilsmann, Philipp Fechteler, Wieland Morgenstern, Wolfgang Paier,
Ingo Feldmann, Oliver Schreer, Peter Eisert
- Abstract summary: We present an end-to-end pipeline for the creation of high-quality animatable volumetric video content of human performances.
Semantic enrichment and geometric animation ability are achieved by establishing temporal consistency in the 3D data.
For pose editing, we exploit the captured data as much as possible and kinematically deform the captured frames to fit a desired pose.
- Score: 7.7824496657259665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we present an end-to-end pipeline for the creation of
high-quality animatable volumetric video content of human performances. Going
beyond the application of free-viewpoint volumetric video, we allow
re-animation and alteration of an actor's performance through (i) the
enrichment of the captured data with semantics and animation properties and
(ii) applying hybrid geometry- and video-based animation methods that allow a
direct animation of the high-quality data itself instead of creating an
animatable model that resembles the captured data. Semantic enrichment and
geometric animation ability are achieved by establishing temporal consistency
in the 3D data, followed by an automatic rigging of each frame using a
parametric shape-adaptive full human body model. Our hybrid geometry- and
video-based animation approaches combine the flexibility of classical CG
animation with the realism of real captured data. For pose editing, we exploit
the captured data as much as possible and kinematically deform the captured
frames to fit a desired pose. Further, we treat the face differently from the
body in a hybrid geometry- and video-based animation approach where coarse
movements and poses are modeled in the geometry only, while very fine and
subtle details in the face, often lacking in purely geometric methods, are
captured in video-based textures. These are processed to be interactively
combined to form new facial expressions. On top of that, we learn the
appearance of regions that are challenging to synthesize, such as the teeth or
the eyes, and fill in missing regions realistically in an autoencoder-based
approach. This paper covers the full pipeline from capturing and producing
high-quality video content, over the enrichment with semantics and deformation
properties for re-animation and processing of the data for the final hybrid
animation.
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