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Data driven auto-completion for keyframe animation Xinyi , Zhang
Abstract
Keyframing is the main method used by animators to choreograph appealing motions, but the process is tedious and labor-intensive. In this thesis, we present a data-driven autocompletion method for synthesizing animated motions from input keyframes. Our model uses an autoregressive two-layer recurrent neural network that is conditioned on target keyframes. Given a set of desired keys, the trained model is capable of generating a interpolating motion sequence that follows the style of the examples observed in the training corpus. We apply our approach to the task of animating a hopping lamp character and produce a rich and varied set of novel hopping motions using a diverse set of hops from a physics-based model as training data. We discuss the strengths and weaknesses of this type of approach in some detail.
Item Metadata
Title |
Data driven auto-completion for keyframe animation
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2018
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Description |
Keyframing is the main method used by animators to choreograph appealing motions,
but the process is tedious and labor-intensive. In this thesis, we present a
data-driven autocompletion method for synthesizing animated motions from input
keyframes. Our model uses an autoregressive two-layer recurrent neural network
that is conditioned on target keyframes. Given a set of desired keys, the trained
model is capable of generating a interpolating motion sequence that follows the
style of the examples observed in the training corpus.
We apply our approach to the task of animating a hopping lamp character and
produce a rich and varied set of novel hopping motions using a diverse set of hops
from a physics-based model as training data. We discuss the strengths and weaknesses
of this type of approach in some detail.
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Genre | |
Type | |
Language |
eng
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Date Available |
2018-08-23
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0371204
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2018-09
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International