a deep learning framework for editingtheorangeduck.com/media/uploads/other_stuff/motion... ·...
TRANSCRIPT
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A Deep Learning Framework for
Character Motion Synthesis and
Editing
Daniel Holden *, Jun Saito †, Taku Komura *,
*The University of Edinburgh
†Marza Animation Planet
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Outline
Motivation
Synthesis
Editing
Discussion
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Goal
Data driven synthesis of motion
from high level controls with
no manual preprocessing
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Previous Work
• Lots of manual processing (Graphs, Trees)
• Segmentation
• Alignment
• Classification
[Heck et al. 2007] [Kovar et al. 2002]
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Previous Work
• Scalability Issues
(RBF, GP, GPLVM, kNN)
• Must store whole database
in memory
• Grows O(n²) with number of
data points
• Requires expensive
acceleration structures
[Park et al. 2002]
[Mukai and Kuriyama 2005]
[Lee et al. 2010]
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Previous Work
• Instability issues
(GPDM, CRBM, RNN)
• Limited to some classes of
motion
• Can suffer high frequency
noise or “dying out”
[Wang et al. 2005]
[Taylor et al. 2011]
[Levine et al. 2012]
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Previous Work
• Deep Neural Network
Hidden Units used to
represent motion
• Denoising
• Retrieval
• Interpolation [Holden et al. 2015]
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Previous Work
• Deep Learning not
always ready for
production
• Results can look
strange
[Radford et al. 2015]
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Contribution
• High quality synthesis with no manual
preprocessing
• Motion synthesis and editing in unified
framework
• Procedural, parallel technique
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Overview
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Overview
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Overview
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Outline
Motivation
Synthesis
Editing
Discussion
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Convolutional Neural Networks
• Great success in classification and
segmentation for images, video, sound
• We can use CNN on motion data too
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ConvolutionFilters convolve over temporal dimension
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ConvolutionFilters convolve over temporal dimension
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ConvolutionFilters convolve over temporal dimension
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ConvolutionFilters convolve over temporal dimension
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ConvolutionFilters convolve over temporal dimension
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What Happened?
• Ambiguity: the same control signal maps
to multiple motions
• These motions are averaged in the output
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Foot Contact
• Contact times resolve
ambiguity
• Automatically label using foot
speed and height
• Learn model that generates
contact times from trajectory
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Foot Contact
Contact Durations
Contact Frequencies
Trajectory Contact Timings
• Use small neural network to map trajectories to
contact durations and frequencies
• Produce timings from durations and frequencies
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Overview
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Outline
Motivation
Synthesis
Editing
Discussion
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Motion Editing
Once motion is generated it must be edited
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Motion Editing
Post processing may not ensure naturalness
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Motion Editing
• We edit using the motion
manifold learned by a
Convolutional Autoencoding
Network [Holden et al. 2015]
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Autoencoder
• Learns projection operator of motion manifold
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Manifold Surface
• Hidden Unit values parametrise manifold surface
• Adjusting them ensures motion remains natural
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Constraint Satisfaction
• Motion editing is a constraint satisfaction
problem over Hidden Units
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Constraint Satisfaction
• Local foot velocity must equal global velocity
• Output trajectory must equal input trajectory
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Overview
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A Neural Algorithm of Artistic Style
• Combine style of
one image with
content of another
[Gatys et al. 2015]
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Style Constraint
• Gram Matrix of Hidden Units encode style
• Actual Values of Hidden Units encode content
• No correspondence between clips required!
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Style Constraint
• Gram Matrix of Hidden Units encode style
• Actual Values of Hidden Units encode content
• No correspondence between clips required!
Content Term
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Style Constraint
• Gram Matrix of Hidden Units encode style
• Actual Values of Hidden Units encode content
• No correspondence between clips required!
Style Term
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Style Constraint
• Gram Matrix of Hidden Units encode style
• Actual Values of Hidden Units encode content
• No correspondence between clips required!
Gram Matrix
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Style Constraint
• Gram Matrix of Hidden Units encode style
• Actual Values of Hidden Units encode content
• No correspondence between clips required!
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Outline
Motivation
Synthesis
Editing
Discussion
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Representation
• We use joint positions local to hip
• Global rotation / translation removed
using hips and shoulders direction
• Root velocity and contact times
appended to representation
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Training
• Motion Manifold
• Several large databases (including whole CMU)
• Training takes around 6 hours
• Motion Synthesis
• Task specific data only (e.g. locomotion only)
• Training takes around 1 hour
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Procedural vs Simulated
• Procedural
• Output computed
at arbitrary times or
in parallel
• Ideal for precise
animation
• Simulated
• Output computed
frame by frame in
series
• Ideal for interactive
applications
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Performance
• Using GPU system runs in parallel over frames
• Very fast for long motions or many characters
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Performance
• Using GPU system runs in parallel over frames
• Very fast for long motions or many characters
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Future Work
• Need more general solution for ambiguity
issue
• Wish to use more high level features with
a deeper network
• What changes are required for interactive
applications?
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