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
Hand pose estimation and hand shape classification using multi-layered randomized decision forests
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Differential evolution based human body pose estimation from point clouds
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Motion capture and human pose reconstruction from a single-view video sequence
Digital Signal Processing
An adaptable system for RGB-D based human body detection and pose estimation
Journal of Visual Communication and Image Representation
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This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then refined by directly fitting the body configuration with the observation (e.g., the input depth). In addition to the new system architecture, our other contributions include modifying a point cloud smoothing technique to deal with very noisy input depth maps, a point cloud alignment and pose search algorithm that is view-independent and efficient. Experiments on a public dataset show that our approach achieves significantly higher accuracy than previous state-of-art methods.