geometry videos symposium on computer animation 2003
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Geometry Videos Symposium on Computer Animation 2003. Hector M. Briceño Collaborators: Pedro V. Sander, Leonard McMillan, Steven Gortler, and Hugues Hoppe. Motivation. Many sources of 3D Animation data: Motion Capture Visual Hulls Physical Simulations Sensor Data Skilled Animators - PowerPoint PPT PresentationTRANSCRIPT
Geometry VideosSymposium on Computer Animation 2003
Hector M. BriceñoCollaborators: Pedro V. Sander, Leonard McMillan, Steven
Gortler, and Hugues Hoppe
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Motivation Many sources of 3D
Animation data: Motion Capture Visual Hulls Physical Simulations Sensor Data Skilled Animators
Wide variety of formats, data, and reconstruction schemes…
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Problem: Sharing 3D Animations
Render a Video of the animation Use the similar software and/or
hardware Use static mesh compression for
each frame DEMO DEMO
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Approach: By representing manifold 3D objects using
a global 2D parametrization (mapping) it is possible to use existing video techniques to represent 3D animations.
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Assumptions of Geometry Videos
One or more manifold surfaces Consistent connectivity through the
duration of the animation No changes in topology Can undergo arbitrary deformations
as well as rigid-body transformations
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Outline Related Work Geometry Images and Geometry
Videos Cuts Parametrization Compression
Exploiting Temporal Coherence Results Future Work and Conclusions
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Related Work: Mesh Compression
Maintaining connectivity: Topological Surgery
[Taubin98] Progressive Meshes
[Hoppe96] Spectral Compression
[Karni00] Re-parametrizing:
Semi-regular: Progressive Compression [Khodakovsky00]
Fully regular: Geometry Images: fully regular [Gu02]
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Related Work: Animated Meshes MPEG4, VRML Animated Meshes “Multi-Resolution Dynamic Meshes with Arbitrary
Deformations” [Shamir00] “Representing Animations by PCA” [Alexa00] “Compression of Time-dependent geometry”
[Lengyel99] “Dynapack” [Ibarria03]
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Related Work: Video MPEG
Spatial, Temporal, SNR Scalability, Motion Compensation, High Compression, VBR…
Other… Layered Coding L-DCT [Amir96] Multi-resolution Video [Finkelstein96]
LOD both time and space. NAIVE [Briceno99]
Graceful degradation, error resilience
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Geometry Images Represents a manifold surface in 3D
space as an 2D array of 3D points. Works in 3 steps:
Cutting: maps 3D surfaces to manifold Parametrization
Maps 3D space -> 2D parameter space Rasterization and Compression
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Parametrization Maps 3D manifold surface onto 2D
square Different criteria or metrics: Conformal,
Area-preserving, Geometric-Stretch
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Rasterization/Compression Sample points of parametrization
obtain a 2D grid of triplets (x,y,z) Compress resulting “image”
DEMO
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Cutting: Geometry Image Iteratively Cut and Reparametrize
Final
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Animated Meshes: Approach
How do we cut, parametrize and compress considering a time-sequence of meshes?
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Cutting: Animations Animation frames should have the
same cut and parametrization
No Correspondence
c
Different Cuts and Parametrization
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Cuts, how to pick? Looking at single frame might miss
something?
Approach: find a global cut considering all frames.
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Global Cut Cut from frame
2 misses spike on frame 1 and spikes on frame 3
Cut
2
Glo
bal C
ut
Frame 1 Frame 2 Frame 3
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Global Cut: how it works Run the iterative algorithm on all frames at
the same time. Pick worst avg. face on all
parametrizations…
Fram
e 1
Fram
e 2
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Parametrization: Animation Cut and parametrization has
to be fixed for all frames in order to use one texture for whole animation
We currently apply the global cut to the first frame and compute parametrization on that frame.
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Compression Spatial Compression:
Wavelets: Can support multiple levels of detail…
Temporal Compression Predictive Coding similar to MPEG Use affine transformations for predictor
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Encoder Architecture
Basic Delta Encoder Uses affine transformations
ReferenceFrame
InputFrame
Cut &Parametrize
Rasterize/Encode Diff
Transform
Decode
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Transformations: Global Global Trans. form a good
approximation
Frame 2Frame 1 Transformed Frame 1
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Transformations: Global con’t Global cannot capture well
deformations within the object
Frame 1
Frame 2
Predictor of Frame 2 from Frame 1
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Transformations: Local Apply transformation on
chartsFrame 1
Frame 2
Predictor
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Transformations: Local w/ Spread & Blend
Spread. Include neighbors in the computation of the transformation
Blend between patches.Target
PredictorNo blendNo spread
Predictorw/blendw/spread
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Results Comparing Geometry Images Comparison to PCA Predictive Coding: Transformations
Global Local
Timing/Performance Level of Detail
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Comparing Geometry Images: Snake
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Comparison to PCA
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Transformations: Global vs. Local
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Transformation Performance
DEMO
2bpv P
8bpv I Baseline
4bpv P
8bpv P8bpv B
s d
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Performance Timings Finding Cut (one frame): 2-7 mins Finding Cut (100 frames): 3-5 hrs Parametrization: 2-6 mins Encoding: 2-3 fps @ 256x256 Encoding: 6-16 fps @ 64x64 Decoding: 10 fps @ 256x256 Decoding: 30-60 fps @ 64x64
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Level of Detail
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Future Work Video Compression Transformations Chartification Parametrization Non-manifold objects
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Conclusions Geometry Video as way to encode
and represent 3D animations Can use many of the 2D Video
Techniques/Features Spatial/Temporal scalability Error resiliency
Many other features to be exploited, i.e. fast clipping and hardware implementation…
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Acknowledgements Collaborators: Pedro Sander,
Leonard McMillan, Steven Gortler, Hughes Hoppe, and Gu Xianfen.
Animations: Matthias Mueller and Daniel VlasicQuestions
?