Augmented Reality with Human Body Interaction Based on Monocular 3D Pose Estimation

We present an augmented reality interface with markerless human body interaction. It consists of 3D motion capture of the human body and the processing of 3D human poses for augmented reality applications. A monocular camera is used to acquire the images of the user’s motion for 3D pose estimation. In the proposed technique, a graphical 3D human model is first constructed. Its projection on a virtual image plane is then used to match the silhouettes obtained from the image sequence. By iteratively adjusting the 3D pose of the graphical 3D model with the physical and anatomic constraints of the human motion, the human pose and the associated 3D motion parameters can be uniquely identified. The obtained 3D pose information is then transferred to the reality processing subsystem and used to achieve the marker-free interaction in the augmented environment. Experimental results are presented using a head mounted display.

[1]  Nicholas R. Howe,et al.  Silhouette lookup for monocular 3D pose tracking , 2007, Image Vis. Comput..

[2]  Tieniu Tan,et al.  Kinematics-based tracking of human walking in monocular video sequences , 2004, Image Vis. Comput..

[3]  Tobias Höllerer,et al.  Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality , 2009, IEEE Transactions on Visualization and Computer Graphics.

[4]  Éric Marchand,et al.  Real-time markerless tracking for augmented reality: the virtual visual servoing framework , 2006, IEEE Transactions on Visualization and Computer Graphics.

[5]  Jitendra Malik,et al.  Twist Based Acquisition and Tracking of Animal and Human Kinematics , 2004, International Journal of Computer Vision.

[6]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[7]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[8]  Raghu Machiraju,et al.  Markerless monocular motion capture using image features and physical constraints , 2005, International 2005 Computer Graphics.

[9]  Stefan Carlsson,et al.  Monocular 3D Reconstruction of Human Motion in Long Action Sequences , 2004, ECCV.

[10]  David Minnen,et al.  The perceptive workbench: Computer-vision-based gesture tracking, object tracking, and 3D reconstruction for augmented desks , 2003, Machine Vision and Applications.

[11]  Henry Been-Lirn Duh,et al.  Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.

[12]  Dieter Schmalstieg,et al.  Finger tracking for interaction in augmented environments , 2001, Proceedings IEEE and ACM International Symposium on Augmented Reality.

[13]  Hirokazu Kato,et al.  Marker tracking and HMD calibration for a video-based augmented reality conferencing system , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[14]  Ronald Azuma,et al.  Recent Advances in Augmented Reality , 2001, IEEE Computer Graphics and Applications.

[15]  Ning Hu,et al.  Training for physical tasks in virtual environments: Tai Chi , 2003, IEEE Virtual Reality, 2003. Proceedings..

[16]  Taku Komura,et al.  Immersive performance training tools using motion capture technology , 2007, IMMERSCOM.