Ипотпал PrePrint: Example-Based Human Motion Denoising
With the proliferation of motion capture data, interest in removing noise and outliers from motion capture data has increased. In this paper, we introduce an efficient human motion denoising technique for the simultaneous removal of noise and outliers from input human motion data. The key idea of our approach is to learn a series of filter bases from precaptured motion data and use them along with robust statistics techniques to filter noisy motion data. Mathematically, we formulate the motion denoising process in a nonlinear optimization framework. The objective function measures the distance between the noisy input and the filtered motion in addition to how well the filtered motion preserves spatial-temporal patterns embedded in captured human motion data. Optimizing the objective function produces an optimal filtered motion that keeps spatial-temporal patterns in captured motion data. We also extend the algorithm to fill in the missing values in input motion data. We demonstrate the effectiveness of our system by experimenting with both real and simulated motion data. We also show the superior performance of our algorithm by comparing it with three baseline algorithms and to those in state-of-art motion capture data processing software such as Vicon Blade.
Подобни новини:
- Ипотпал PrePrint: Robust Feature-Preserving Mesh Denoising Based on Consistent SubneighborhoodsIn this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise...
- Ипотпал PrePrint: A Dynamic Texture Based Approach to Recognition of Facial Actions and their Temporal ModelsIn this work we propose a dynamic-texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences...
- Ипотпал PrePrint: Bilayer Segmentation of Webcam Videos Using Tree-Based ClassifiersThis paper presents an automatic segmentation algorithm for video frames captured by a monocular webcam. The frames are segmented into foreground and background layers that...
- Ипотпал PrePrint: Multibody Structure-from-Motion in PracticeMultibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of...
- Ипотпал PrePrint: Garbage Collection for Flexible Hard Real-Time SystemsHard real-time systems always choose not to use garbage collection in order to avoid their unpredictable executions. Much effort has been expended trying to build...
- Ипотпал PrePrint: Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation LearningNegative Correlation Learning (NCL) is a neural network ensemble learning algorithm which introduces a correlation penalty term into the cost function of each individual network...
- Ипотпал PrePrint: Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted TrajectoriesIn this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera...
