Light Field Video Synthesis Using Inexpensive Surveillance Camera Systems

We present a light field video synthesis technique that can achieve accurate reconstruction given a low-cost, widebaseline camera rig. Our system called, INDiuM, novelly integrates optical flow with methods for rectification, disparity estimation, and feature extraction, which we then feed to a neural network view synthesis solver with widebaseline capability. A new bi-directional warping approach resolves reprojection ambiguities that would result from either backward or forward warping only. The system and method enables the use of off-the-shelf surveillance camera hardware in a simplified and expedited capture workflow. A thorough analysis of the refinement process and resulting view synthesis accuracy over state of the art is provided.

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Smile Intensity Detection in Multiparty Interaction using Deep Learning

Emotion expression recognition is an important aspect for enabling decision making in autonomous agents and systems designed to interact with humans. In this paper, we present our experience in developing a software component for smile intensity detection for multiparty interaction. First, the deep learning architecture and training process is described in detail. This is followed by analysis of the results obtained from testing the trained network. Finally, we outline the steps taken to implement and visualize this network in a real-time software component.

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Disentangled Sequential Autoencoder

In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping.

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