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

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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.

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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.

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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.