a coherent locomotion engine extrapolating beyond experimental data sca ’ 04 speaker: alvin data:...
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A Coherent Locomotion Engine Extrapolating Beyond
Experimental Data
SCA ’04Speaker: Alvin
Data: January 3, 2005
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Outline
• Introduction• Framework• Results• Evaluation Form• Conclusions• Future Works
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
3
Introduction
• Input: MoCap• Output: Locomotion• Animate characters of any size
through high-level parameters.• An on-line reactive method.• Use PCA to reduce computation
cost for Real-time.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Framework
• Normalize Motion• Main PCA• Motion Extrapolation
• Sub-PCA Level 1• Sub-PCA Level 2• Linear Square Fit
• Motion Generation
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Input Data• Record 5 subjects. (2♀ & 3♂)• Walking speed from 3.0 km/h to 7.0
km/h. (Interval is 0.5 km/h)• Running speed from 6.0 km/h to
12.0 km/h. (Interval is 1.0 km/h)• Sequences are segmented into
cycles. (2 steps, starting from right heel strike), and 4 of them are selected
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Normalize Motion
• All 3D positions of motion vectors are divided by the leg length. [Murray67]
• Use frequency function to handle time warp. [InmanRT81]
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Main PCA• A motion can be represented by a
set of joint angle vectors measured at regularly sampled intervals.
• Blending technique can be applied on various α, but not appropriate for motion extrapolation.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Motion Extrapolation
• Speed• Radial Basis Function
• No motion get a zero weight.• Undesired results due to the
influence of other subjects examples.
• All example motions have to be classified by group of similarities (subject, type of locomotion)
• Allow a linear least square in a very low dimension.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Motion Extrapolation – Sub-PCA Level 1
• Use simple clustering method to separate the different subjects.
• Coefficient vectors α grouped by subject are used to apply sub-PCA level 1.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Motion Extrapolation – Sub-PCA Level 1
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
11
Motion Extrapolation – Sub-PCA Level 2
• Coefficient vectors β grouped by type of locomotion are used to apply sub-PCA level 2.
• Two sub-PCA level 2 are needed to avoid giving too much importance to the neutral posture.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Motion Extrapolation – Linear Square Fit
• Determine a relationship between a coefficient vector γ and its corresponding speed value.
• Find the approximation functionby
minimizing the sum of square distances.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Motion Extrapolation – Linear Square Fit
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Motion Generation• Speed (S)• Type of Locomotion (T)• Personification (p)• Human Size (H)• Transition
• After motions are normalized, cut into cycles, frame i for walking near frame i for running.
• Accord to T.• Real-time
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Results
• Data compression rate is 25.• Preprocess spends 13 sec.• New motion (100 frames) generation
spends 1.5 ms.• CPU 1.8 GHz
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Results
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Evaluation Form• 論文簡報部份
• 完整性介紹 (4)• 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 (4)• 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：
We can use PCA to reduce high-dimension motion data and extract the features of motions. Because the hierarchical structure of PCA can help the classification, so I will try to adapt it into my method. Besides, the description about motion represented as motion vector can be referenced in my paper.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Conclusions
• Experimental data classification• Space and time normalization• Generate locomotion sequences at
each parameter update.• Real-time• Allow quantitative extrapolation.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Future Works• Motion Prediction
• CD for the foot• Jump over an obstacle or walk around
it
• RBF can be added to linear fitting.• Apply to other locomotion types.
Alivn/GAME Lab./CSIE/NDHU
A Coherent Locomotion Engine Extrapolating Beyond Experimental Data
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Radial Basis Function• A linear combination of translates of basis f
unctions, the basis functions being invariant with respect to rotations on the underlying space.