Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synchronized submanifold embedding for person-independent pose estimation and beyond
IEEE Transactions on Image Processing
Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
International Journal of Computer Vision
International Journal of Computer Vision
Coupled Visual and Kinematic Manifold Models for Tracking
International Journal of Computer Vision
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
Physics-Based Person Tracking Using the Anthropomorphic Walker
International Journal of Computer Vision
A software pipeline for 3D animation generation using mocap data and commercial shape models
Proceedings of the ACM International Conference on Image and Video Retrieval
Dual gait generative models for human motion estimation from a single camera
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Shared Kernel Information Embedding for Discriminative Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
Hi-index | 0.00 |
This paper presents a two-layer gait representation framework for video-based human motion estimation that extends our recent dual gait generative models, visual gait generative model (VGGM) and kinematic gait generative model (KGGM), with a new capability of part-whole gait modeling. Specifically, the idea of gait manifold learning is revisited to capture the gait variability among different individuals at both whole and part levels. A key issue is the selection of an appropriate distance metric to evaluate the dissimilarity between two gaits (either at whole or part levels) that determines an optimal manifold topology. Several metrics are studied and compared in terms of their effectiveness for gait manifold learning at both whole and part levels. This work involves one whole-based and two part-level gait manifolds by which three pairs of KGGM and VGGM can be learned and integrated for part-whole gait modeling. Moreover, a two-stage Monte Carlo Markov Chain (MCMC) inference algorithm is developed for video-based part-whole motion estimation. The proposed algorithm is tested on the HumanEva data and reaches state-of-art results.