PPT-3d Pose Detection

Used by Kinect Accurate when the pose closely matches a stored pose Inaccurate when novel poses are made Can often produce shaky movement due to pose snapping 3d Pose Tracking Calculate poses based on previous poses and current data. 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 After a brief introductionmotivation for the need for parts the bulk of the chapter will be split into three core sections on Representation Inference and Learning We begin by describing various gradient based and color descriptors for parts We will 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. 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. Leonid . Pishchulin. . . Arjun. Jain. . Mykhaylo. . Andriluka. Thorsten . Thorm¨ahlen. . Bernt. . Schiele. Max . Planck Institute for Informatics, . Saarbr¨ucken. , Germany. Introduction. Generation of novel training . Ning. Zhang. 1,2. . . Manohar. . Paluri. 1. . . Marć. Aurelio . Ranzato. . 1. . Trevor Darrell. 2. . . Lumbomir. . Boudev. 1. . 1. . Facebook AI Research . 2. . EECS, UC Berkeley. Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Ning. Zhang. 1,2. . . Manohar. . Paluri. 1. . . Marć. Aurelio . Ranzato. . 1. . Trevor Darrell. 2. . . Lumbomir. . Boudev. 1. . 1. . Facebook AI Research . 2. . EECS, UC Berkeley. Pelvic Tilt. 3. Bridge Pose. 4. Rocking. 5. Little Boat Twist. 6. Cat. 7. Swan. 8. Table Balancing Pose. 9. Thread the Needle. 10. Cobra. 11 . Corpse. Rocking. Swan. 1. 2. 3. 4. 5. 6. 7. 8. 9. 0. 1. Chair. Xiao Sun. Joint work with Yichen Wei. Human Pose Estimation. Problem: localize key points of a person. Input: a single RGB image. Output: 2D or 3D key points. Pose Estimator. RGB Image (person centered). Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Xiao Sun. https://jimmysuen.github.io./. Microsoft Research Asia. Visual Computing Group. Human Pose Estimation. Problem: localize key points of a person. Input: a single RGB image. Output: 2D or 3D key points. 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. .