We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style preservation. In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. We then demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.

Input&Images&Predicted&surface&normals& CAD&model&library& Aligned&models& Figure 1. Given a single 2D image, we predict surface normals that capture detailed object surfaces. We use the image and predicted surface normals to retrieve a 3D model from a large library of object CAD models. AbstractWe introduce an approach that leverages surface normal predictions, along with appearance cues, to retrieve 3D models for objects depicted in 2D still images from a large CAD object library. Critical to the success of our approach is the ability to recover accurate surface normals for objects in the depicted scene. We introduce a skip-network model built on the pre-trained Oxford VGG convolutional neural network (CNN) for surface normal prediction. Our model achieves state-of-the-art accuracy on the NYUv2 RGB-D dataset for surface normal prediction, and recovers fine object detail compared to previous methods. Furthermore, we develop a two-stream network over the input image and predicted surface normals that jointly learns pose and style for CAD model retrieval. When using the predicted surface normals, our two-stream network matches prior work using surface normals computed from RGB-D images on the task of pose prediction, and achieves state of the art when using RGB-D input. Finally, our two-stream network allows us to retrieve CAD models that better match the style and pose of a depicted object compared with baseline approaches.

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Abstract. Today's computer vision systems are not perfect. They fail frequently. Even worse, they fail abruptly and seemingly inexplicably. We argue that making our systems more transparent via an explicit human understandable characterization of their failure modes is desirable. We propose characterizing the failure modes of a vision system using semantic attributes. For example, a face recognition system may say "If the test image is blurry, or the face is not frontal, or the person to be recognized is a young white woman with heavy make up, I am likely to fail." This information can be used at training time by researchers to design better features, models or collect more focused training data. It can also be used by a downstream machine or human user at test time to know when to ignore the output of the system, in turn making it more reliable. To generate such a "specification sheet", we discriminatively cluster incorrectly classified images in the semantic attribute space using L1-regularized weighted logistic regression. We show that our specification sheets can predict oncoming failures for face and animal species recognition better than several strong baselines. We also show that lay people can easily follow our specification sheets.