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A Data-Driven Approach to Quantifying Natural Human Motion

A Data-Driven Approach to Quantifying Natural Human Motion. SIGGRAPH ’ 05 Liu Ren, Alton Patrick, Alexei A. Efros, Jassica K. Hodgins, and James M. Rehg Carnegie Mellon University & Georgia Institute of Technology Date: 8/24/2005 Speaker: Alvin. Outline. Introduction Input Data

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A Data-Driven Approach to Quantifying Natural Human Motion

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  1. A Data-Driven Approach to Quantifying Natural Human Motion SIGGRAPH ’05 Liu Ren, Alton Patrick, Alexei A. Efros, Jassica K. Hodgins, and James M. Rehg Carnegie Mellon University & Georgia Institute of Technology Date: 8/24/2005 Speaker: Alvin

  2. Outline • Introduction • Input Data • Approaches • Results • Conclusions & Future Works A Data-Driven Approach to Quantifying Natural Human Motion

  3. Introduction • Goal • Quantify the naturalness of human motion • Solution • Train a classifier based on human-labeled data • Train only on positive examples • Assumption • Motions that we have seen repeatedly are judged as natural A Data-Driven Approach to Quantifying Natural Human Motion

  4. Introduction cont. • Application • Verify the motion editing operations • Contribution • Pose the question • Decompose human motion into constituent parts • Contribute a substantial database A Data-Driven Approach to Quantifying Natural Human Motion

  5. Outline • Introduction • Input Data • Approaches • Results • Conclusions & Future Works A Data-Driven Approach to Quantifying Natural Human Motion

  6. Input Data • Training Database • Testing Motions A Data-Driven Approach to Quantifying Natural Human Motion

  7. Training Database • 1289 trials (422,413 frames) • 34 subjects • Vicon motion capture system of 12 cameras • Downsample from 120Hz to 30 Hz • 41 markers A Data-Driven Approach to Quantifying Natural Human Motion

  8. Data Format • ASF/AMC format • Root position • Root orientation • Relative joint angles of 18 joints • 151-dimensional feature vector • Joint angle (4) and velocity (4) • Root’s linear velocity(3) and angular velocity(4) A Data-Driven Approach to Quantifying Natural Human Motion

  9. Testing Motions - Negative • 170 trials, 27774 frames • Edited motions • Keyframed motions • Noise • Motion Transitions • Insufficient cleaned motion capture data A Data-Driven Approach to Quantifying Natural Human Motion

  10. Testing Motions - Positive • 261 trials, 92377 frames • MoCap • Noise • Motion Transitions • Judge by an expert viewer A Data-Driven Approach to Quantifying Natural Human Motion

  11. Outline • Introduction • Input Data • Approaches • Results • Conclusions & Future Works A Data-Driven Approach to Quantifying Natural Human Motion

  12. Approaches • Framework • Mixture of Gaussians • Hidden Markov Models • Switching Linear Dynamic System • Naive Bayes (baseline method) • User Study A Data-Driven Approach to Quantifying Natural Human Motion

  13. Framework • Select the statistical model • Fit the model parameters using natural human motions as training data • Compute a score for a novel input motion A Data-Driven Approach to Quantifying Natural Human Motion

  14. Ensemble 8 24 8 8 8 8 8 31 8 24 24 8 8 72 4+3 8 8 8 8 24 24 8 8 8 8 A Data-Driven Approach to Quantifying Natural Human Motion

  15. Advantages of ensemble • Avoid the problem of overfitting • Detect the unnatural motions confined to a small set of joint angles • Provide guidance about what elements deserve the most attention A Data-Driven Approach to Quantifying Natural Human Motion

  16. Scoring A Data-Driven Approach to Quantifying Natural Human Motion

  17. Mixture of Gaussians (MoG) • The combinations of a finite number of Gaussian distributions • Used to model complex multidimensional distributions • EM algorithm is used to learn the parameters of the Gaussian mixture A Data-Driven Approach to Quantifying Natural Human Motion

  18. MoG cont. • 500 Gaussians • Weak at modeling the dynamics A Data-Driven Approach to Quantifying Natural Human Motion

  19. Hidden Markov Models (HMM) A Data-Driven Approach to Quantifying Natural Human Motion

  20. HMM cont. A Data-Driven Approach to Quantifying Natural Human Motion

  21. HMM cont. • The distribution of poses is represented with a mixture of Gaussians • State was modeled as a single Gaussian • Parameters are learned by EM • 180 hidden states for full body • 60 hidden states for other feature group A Data-Driven Approach to Quantifying Natural Human Motion

  22. Switching Linear Dynamic System (SLDS) • State is associated with LDS instead of Gaussian distribution • Second-order auto-regressive (AR) model • Initial state is described by MoG • Parameters are estimated using EM • 50 switching states for full body • 5 switching states for other feature group A Data-Driven Approach to Quantifying Natural Human Motion

  23. Principal Component Analysis • HMM & SLDS • 99% variance kept for the full-body model • 99.9% variance kept for the smaller model A Data-Driven Approach to Quantifying Natural Human Motion

  24. Naive Bayes (NB) • Compute 1D marginal histogram for each feature over the entire training database • Each histogram has 300 buckets • Summing over the log likelihoods of each of the 151 features for each frame • Nomalizing the sum by the length A Data-Driven Approach to Quantifying Natural Human Motion

  25. User Study • 29 ♂ & 25 ♀ • 118 motion sequences • 2 segments with a 10 minute break • The order of sequences is random A Data-Driven Approach to Quantifying Natural Human Motion

  26. Outline • Introduction • Input Data • Approaches • Results • Conclusions & Future Works A Data-Driven Approach to Quantifying Natural Human Motion

  27. Results A Data-Driven Approach to Quantifying Natural Human Motion

  28. Receiver Operating Characteristic Curve (ROC curve) • False positive • Classifier predicts natural when the motion is unnatural • True positive rate • tp / (tp + fn) • False positive rate • fp / (fp + tn) • Without need to choose a threshold • The more area under the ROC curve, the more accurate the test A Data-Driven Approach to Quantifying Natural Human Motion

  29. A Data-Driven Approach to Quantifying Natural Human Motion

  30. A Data-Driven Approach to Quantifying Natural Human Motion

  31. A Data-Driven Approach to Quantifying Natural Human Motion

  32. A Data-Driven Approach to Quantifying Natural Human Motion

  33. Demo A Data-Driven Approach to Quantifying Natural Human Motion

  34. Outline • Introduction • Input Data • Approaches • Results • Conclusions & Future Works A Data-Driven Approach to Quantifying Natural Human Motion

  35. Conclusions • Unusual motions are sometimes labeled unnatural (like falling) • Short errors and slow motions may not be detected • Used to improve the performance of motion synthesis and motion editing tools A Data-Driven Approach to Quantifying Natural Human Motion

  36. Future Works • Explore dimensionality reduction approaches for SLDS model • More sophisticated methods for normalizing or computing the score • Screening for the style of a particular cartoon character A Data-Driven Approach to Quantifying Natural Human Motion

  37. Thank you for your attention A Data-Driven Approach to Quantifying Natural Human Motion

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