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Understanding the camera wearers activity is central to egocentric vision, yet one key facet of that activity is inherently invisible to the camera—the wearers body pose. Prior work focuses on estimating the pose of hands and arms when they come into view, but this 1) gives an incomplete view of the full body posture, and 2) prevents any pose estimate at all in many frames, since the hands...
We present a Bayesian framework for estimating 3D human pose and camera from a single RGB image. We develop a generative model where a 3D pose is rendered onto an image (via the camera), which then generates a detection probability map for each body part. We represent a human pose with a set of 3D cylinders in space, one for each body part, and we place kinematic and self-intersection priors on the...
We propose a deep convolutional neural network for 3Dhuman pose and camera estimation from monocular imagesthat learns from 2D joint annotations. The proposed networkfollows the typical architecture, but contains an additionaloutput layer which projects predicted 3D joints onto2D, and enforces constraints on body part lengths in 3D.We further enforce pose constraints using an independentlytrained...
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