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We present a novel approach for animating static images that contain objects that move in a subtle, stochastic fashion (e.g. rippling water, swaying trees, or flickering candles). To do this, our algorithm leverages example videos of similar objects, supplied by the user. Unlike previous approaches which estimate motion fields in the example video to transfer motion into the image, a process which...
Cameras are becoming increasingly aware of the picture-taking context, collecting extra information around the act of photographing. This contextual information enables the computational generation of a wide range of enhanced photographic outputs, effectively expanding the imaging experience provided by consumer cameras. Computer vision and computational photography techniques can be applied to provide...
A class of techniques in computer vision and graphics is based on capturing multiple images of a scene under different illumination conditions. These techniques explore variations in illumination from image to image to extract interesting information about the scene. However, their applicability to dynamic environments is limited due to the need for robust motion compensation algorithms. To overcome...
We propose a novel hierarchical structured prediction approach for ranking images of faces based on attributes. We view ranking as a bipartite graph matching problem; learning to rank under this setting can be achieved through structured prediction techniques that directly optimize the matching measures. Our key contribution is a novel model that combines structured predictors for different feature...
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