Accurate realtime full-body motion capture using a single depth camera
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Unsupervised human skeleton extraction from Kinect depth images
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Performance capture of interacting characters with handheld kinects
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Robust 3d action recognition with random occupancy patterns
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Real-time human pose tracking from range data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Content-aware exaggerated editing for life-like captured animations
Proceedings of the 9th European Conference on Visual Media Production
Computer Vision and Image Understanding
Using Segmented 3D Point Clouds for Accurate Likelihood Approximation in Human Pose Tracking
International Journal of Computer Vision
Differential evolution based human body pose estimation from point clouds
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Model based full body human motion reconstruction from video data
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
HandSonor: a customizable vision-based control interface for musical expression
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization
The Visual Computer: International Journal of Computer Graphics
Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image
Proceedings of the Fourth Symposium on Information and Communication Technology
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In recent years, depth cameras have become a widely available sensor type that captures depth images at real-time frame rates. Even though recent approaches have shown that 3D pose estimation from monocular 2.5D depth images has become feasible, there are still challenging problems due to strong noise in the depth data and self-occlusions in the motions being captured. In this paper, we present an efficient and robust pose estimation framework for tracking full-body motions from a single depth image stream. Following a data-driven hybrid strategy that combines local optimization with global retrieval techniques, we contribute several technical improvements that lead to speed-ups of an order of magnitude compared to previous approaches. In particular, we introduce a variant of Dijkstra's algorithm to efficiently extract pose features from the depth data and describe a novel late-fusion scheme based on an efficiently computable sparse Hausdorff distance to combine local and global pose estimates. Our experiments show that the combination of these techniques facilitates real-time tracking with stable results even for fast and complex motions, making it applicable to a wide range of inter-active scenarios.