Retrieval, recognition and reconstruction of quadruped motions

Different techniques have been developed for capturing and retrieval, action recognition and video based reconstruction of human motion data in the past years. In this paper, we focus on how these techniques can be adapted to handle quadruped motion capture data and which new applications may appear. We discuss some particularities that must be considered during large animal motion capture. For retrieval, we derive suitable feature sets from quadrupeds motion capture data to perform fast searches for similar motions. Based on the retrieval techniques, the action recognition can be performed on the input motion capture sequences as well as on input video streams. We further present a data-driven approach to reconstruct quadruped motions from video data.

[1]  Tido Röder,et al.  Documentation Mocap Database HDM05 , 2007 .

[2]  Sandra Nauwelaerts,et al.  Effects of trunk deformation on trunk center of mass mechanical energy estimates in the moving horse, Equus caballus. , 2009, Journal of biomechanics.

[3]  Wen-Chieh Lin,et al.  Real‐time horse gait synthesis , 2013, Comput. Animat. Virtual Worlds.

[4]  A. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, CVPR 2004.

[5]  Tido Röder,et al.  Efficient content-based retrieval of motion capture data , 2005, SIGGRAPH 2005.

[6]  Michael Gleicher,et al.  Automated extraction and parameterization of motions in large data sets , 2004, SIGGRAPH 2004.

[7]  Hans-Peter Seidel,et al.  Motion reconstruction using sparse accelerometer data , 2011, TOGS.

[8]  Reinhard Klein,et al.  Interactive steering of mesh animations , 2012, SCA '12.

[9]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[10]  Hans-Peter Seidel,et al.  Staying Well Grounded in Markerless Motion Capture , 2008, DAGM-Symposium.

[11]  Björn Krüger,et al.  Model based full body human motion reconstruction from video data , 2013, MIRAGE '13.

[12]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Jane Wilhelms,et al.  Combining vision and computer graphics for video motion capture , 2003, The Visual Computer.

[14]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  David M. Levine,et al.  Motion analysis and its use in equine practice and research , 2010 .

[16]  Marie-Paule Cani,et al.  Animal gaits from video , 2004, SCA '04.

[17]  David A. Forsyth,et al.  Motion synthesis from annotations , 2003, ACM Trans. Graph..

[18]  Marie-Paule Cani,et al.  Quadruped Animation , 2008, Eurographics.

[19]  Lucas Kovar,et al.  Automated extraction and parameterization of motions in large data sets , 2004, ACM Trans. Graph..

[20]  Arno Zinke,et al.  Fast local and global similarity searches in large motion capture databases , 2010, SCA '10.

[21]  Jinxiang Chai,et al.  VideoMocap: modeling physically realistic human motion from monocular video sequences , 2010, ACM Trans. Graph..

[22]  Jessica K. Hodgins,et al.  Performance animation from low-dimensional control signals , 2005, SIGGRAPH 2005.