PPT-Real-time Articulated Hand Pose Estimation using Semi-super

Transductive Regression Forests TszHo Yu Danhang Tang TK Kim Sponsored by 2 Motivation Multiple cameras with invserse kinematics Bissacco et al CVPR2007 Yao et al IJCV2012. Rather than modeling articulation using a family of warped rotated and foreshortened templates we use a mixture of small nonoriented parts We describe a general 64258exible mixture model that jointly captures spatial relations between part locations berkeleyedu lubomirfbcom Figure 1 An example of an image where part detectors based solely on strong contours and edges will fail to detect the upper and lower parts of the arms Abstract We propose a novel approach for human pose estimation in realwo Our approach is examplebased it reduces the problem of recovering the pose to a database search under in the embedding space which is carried out extremely fast using LSH The embedding is constructed based on edge direction histograms using the algo Eichner M MarinJimenez A Zisserman V Ferrari Abstract We present a technique for estimating the spatial layout of humans in still images the position of the head torso and arms The theme we explore is that once a per son is localized using an upper Tracking . and Head Pose Estimation for Gaze Estimation. Ankan Bansal. Salman Mohammad. CS365 Project. Guide - Prof. . Amitabha Mukerjee. Motivation. Human Computer Interaction. Information about interest of the subject, e.g. advertisement research. Yi Yang & Deva . Ramanan. University of California, Irvine. Goal. Articulated pose . estimation. ( ). recovers . the pose of an articulated object which consists of joints and rigid parts. Supervisor. : . Dr. . Yiu. . Siu. Ming. Second Examiner. : . Professor Francis . Y.L.. Chin. Student. : Vu . Thi. . Quynh. . Hoa. Contents. Introduction. Motivation. Related Work. Project Plan. Problem Definitions. Yu Chen, Tae-. K. yun. Kim, Roberto . Cipolla. Department of Engineering. University of Cambridge. Roadmap. Brief Introductions. Our Framework. Experimental Results. Summary. Motivation. +. 3D Shapes. Yao Li . Fei-Fei. Computer Science Department, Stanford University, USA. Modeling Mutual Context of Object and Human Pose. in Human-Object Interaction Activities. Introduction. Modeling mutual context of object and pose. Hamed Pirsiavash and Deva . Ramanan. Department of Computer Science. UC Irvine . 2. Deformable . part models . (DPM). Human pose estimation. Face pose estimation. Object detection. Felzenszwalb. , . Girshick. Tompson. , Murphy Stein, Yann . LeCun. , Ken . Perlin. REAL-TIME CONTINUOUS POSE RECOVERY OF . HUMAN HANDS USING CONVOLUTIONAL NETWORKS. Target: low-cost . markerless. . mocap. Full articulated pose with high . Leonid . Pishchulin. . . Arjun. Jain. . Mykhaylo. . Andriluka. Thorsten . Thorm¨ahlen. . Bernt. . Schiele. Max . Planck Institute for Informatics, . Saarbr¨ucken. , Germany. Introduction. Generation of novel training . Yao Lu. Outline. Overview of RGB-D images and sensors. Recognition: human pose, hand gesture. Reconstruction: Kinect fusion. Outline. Overview of RGB-D images and sensors. Recognition: human pose, hand gesture. Rendevous. using CNN. Ryan McKennon-Kelly. Sharma, . Sumant. , Connor . Beierle. , and Simone D’Amico. “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks,” September 19, 2018. .