Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion Reconstruction using Moments Analysis
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
3D Human Motion Reconstruction Using Video Processing
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
VideoMocap: modeling physically realistic human motion from monocular video sequences
ACM SIGGRAPH 2010 papers
Fast local and global similarity searches in large motion capture databases
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Motion reconstruction using sparse accelerometer data
ACM Transactions on Graphics (TOG)
Natural Character Posing from a Large Motion Database
IEEE Computer Graphics and Applications
Three-dimensional proxies for hand-drawn characters
ACM Transactions on Graphics (TOG)
3D reconstruction of human motion and skeleton from uncalibrated monocular video
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
The human motion database: A cognitive and parametric sampling of human motion
Image and Vision Computing
3D reconstruction of a smooth articulated trajectory from a monocular image sequence
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Fast image-based localization using direct 2D-to-3D matching
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A data-driven approach for real-time full body pose reconstruction from a depth camera
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Reconstructing 3d human pose from 2d image landmarks
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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This paper introduces a novel framework for full body human motion reconstruction from 2D video data using a motion capture database as knowledge base containing information on how people move. By extracting suitable two-dimensional features from both, the input video sequence and the motion capture database, we are able to employ an efficient retrieval technique to run a data-driven optimization. Only little preprocessing is needed by our method, the reconstruction process runs close to real time. We evaluate the proposed techniques on synthetic two-dimensional input data obtained from motion capture data and on real video data.