Computer Vision

We present a system for fast and robust handovers with a robot character, together with a user study investigating the effect of robot speed and reaction time on perceived interaction quality. The system can match and exceed human speeds and confirms that users prefer human-level timing.

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.

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.

The paper describes the overall workflow and detailed algorithm of each component, followed by an evaluation validating the proposed method.

This paper proposes and experimentally compares different losses integrating MRF/CRF regularization terms.

In this paper, we describe a complete pipeline for the capture and display of real-world Virtual Reality video content, based on the concept of omnistereoscopic panoramas.

We propose a novel neural network architecture for point cloud classification.

We propose a new approach, PhaseNet, that is designed to robustly handle challenging scenarios while also coping with larger motion.