motion texture: a two-level statistical model for character motion synthesis siggraph ‘02 speaker:...
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Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis
SIGGRAPH ‘02
Speaker: Alvin
Date: 3 August 2004
Alivn/GAME Lab./CSIE/NDHU2
Outline
Introduction Framework Result Conclusion Evaluation Form
Alivn/GAME Lab./CSIE/NDHU3
Introduction
Motion Texture – A two-level statistical model – Texton
Local dynamics Represented by a linear dynamic system (LDS).
– Distribution Global dynamics Modeled by a transition matrix
– Counting how many times a texton is switched to another.
Alivn/GAME Lab./CSIE/NDHU4
Two-level Statistical Model
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Motion Texton
State-Space model (LDS):
Xt – Hidden State Variable
Yt – The Observation
Vt, Wt – Independent Gaussian noises at time t.
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Distribution
Commonly used in HMMs
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Introduction (cont.)
Statistically similar to the original motion. Motion textures display a 1-D temporal
distribution. User can synthesis and edit at both the
texton level and the distribution level.
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Framework
Learning– E-step– M-step
Synthesis– Texton Path Planning– Texton Synthesis
By Sampling Noise With Constrained LDS
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E-step
Segment Labels as L = {l1,l2,…, lNs}
Segmentation points as H = {H1,H2,…,HNs}
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M-step
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Learning
Initialization - A greedy approach:
Until the entire sequence is processed:– Use Tmin to fit LDSi
– Label the subsequent frames to segment i until the fitting error is above a threshold.
– Test all existing LDS’ to choose the best-fit LDS.– If no LDS fits well, introduce a new LDS.
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Learning (cont.)
The bigger the threshold, the longer the segments, and the fewer the number of textons.– Model selection methods:
BIC MDL
Tmin must be long enough to capture the local dynamics.– Approximately one second.
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Texton Path Planning
find a single best path,
, which starts at and ends at .
Two approaches:– Finding the Lowest Cost Path
Dijkstra’s algorithm
– Specifying the Path Length Dynamic Programming
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Texton Synthesis
By sampling noise– Inevitably depart from the original motion as time
progresses. LDS learns only locally consistent motion patterns. The synthesis errors will accumulate as xt propagates.
With constrained LDS– Setting the end constraints.– The in-between frames can be synthesized by sol
ving a block-banded system of linear equations.
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Result
Environment– Intel P4 1.4G– 1G Memory
Input– Capture 20 minutes of dance motion. (49800 frames)
Result– It took about 4 hours to learn.– 246 textons are found.– The length of the texton ranges from 60 to 172 frames.– Synthesizing a texton only 25ms to 35ms. (Real-time)
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Result (cont.)
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Result (cont.)
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Result (cont.)
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Result (cont.)
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Conclusion
Best suited for repeated motions. Lack global variation when the data is limited. Did not incorporate any physical model into
the synthesis algorithm. Capture the essential properties of the
original motion.
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Conclusion (cont.)
The edited pose can not deviate from the original one too much.
The additional constraint may contaminate the synthesized texton.
Does not consider the interaction with environment.
Initialization can be improved.
Alivn/GAME Lab./CSIE/NDHU22
Evaluation Form
論文簡報部份– 完整性介紹 (3)– 系統性介紹 (4)– 表達能力 (3)– 投影片製作 (3)
論文審閱部分– 瞭解論文內容 (3)– 結果正確性與完整性 (4)– 原創性與重要性 (4)– 讀後啟發與應用:
When we meet a problem that its input is highly repeating, we can use the statistical method to find the basic element.
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