Recovering 3D human pose from monocular images
نویسندگان
چکیده
منابع مشابه
Learning Methods for Recovering 3D Human Pose from Monocular Images
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouett...
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Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Manual annotation is tedious, slow, and error-prone. In this paper, we propose to replace most of the annotations by the use of multiple views,...
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Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we...
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Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as Support Vector Machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additio...
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In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: 1) a multi-task framework that jointly trains pose regression and body part detectors; 2) a pre-training strategy where the pose regressor is initialized using a network trained for body part detection. We compare our network on a large dat...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2006
ISSN: 0162-8828
DOI: 10.1109/tpami.2006.21