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Learning a deep model for human action recognition from novel viewpoints

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Fig. 1: Existing cross-view action recognition techniques [15]–[23] connect two different views with a set of linear transfor-mations that are unable to capture the non-linear manifolds onwhich real actions lie
Fig. 2: Framework of the proposed R-NKTM learning algorithm. A realistic 3D human model (a) is fitted to a real mocap sequence(b) to generate 3D action video (c) which is projected to plains viewed from n “ 108 angles
Fig. 3: Virtual cameras are placed on the hemisphere lookingtowards the center of the sphere to generate 108 virtual views.
Fig. 4: Assume that there areviews to a shared high-level virtual view. We show onlyvirtual paths forhigh-level space and the non-linear virtual paths connecting inputviews to this shared space
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