1 / 37

A Data-Driven Approach to Quantifying Natural Human Motion

This paper discusses a data-driven method to quantify natural human motion, focusing on creating a classifier based on human-labeled motion data. The study involves analyzing motions, training on positive examples, and utilizing diverse statistical frameworks, including Mixture of Gaussians, Hidden Markov Models, and Switching Linear Dynamic Systems. Results demonstrate the ability to assess the naturalness of motions, useful for motion editing and animation. The findings contribute to an extensive database that can be leveraged for improving the realism in computer graphics and animation.

wood
Télécharger la présentation

A Data-Driven Approach to Quantifying Natural Human Motion

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related