Human-Computer Interaction

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression.

JUNGLE is an interactive, visual platform for the collaborative manipulation and consumption of nonlinear transmedia stories.

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.

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.

We explore a way to elicit and evaluate affective behavior using crowdsourcing. We show that untrained crowd workers are able to author content for a broad variety of target affect states when given semi-situated narratives as prompts.

This paper addresses this problem. Based on a recently published general purpose wearable BCC system, we first present a thorough evaluation of the impact of various technical parameter choices and an exhaustive channel characterization of the human body as a host for BCC.

We demonstrate the efficacy of the approach through a four-day study in which teams of participants interacted with a social robot expressing one of two personalities as the host of a competitive game.

In this paper, we describe the dialog management mechanisms to achieve these goals when applied to a robot that engages in social chit-chat.