Placement Retargeting of Virtual Avatars to Dissimilar Indoor Environments

TL;DR

This paper introduces methods for accurately placing virtual avatars in dissimilar indoor environments to maintain user position semantics, using neural networks trained on user survey data and features.

Contribution

It presents a novel approach combining user surveys, feature extraction, and neural network prediction to improve avatar placement in varied indoor spaces.

Findings

Neural network effectively predicts placement similarity

Prototype AR telepresence system demonstrates practical viability

User evaluations confirm improved avatar placement accuracy

Abstract

Rapidly developing technologies are realizing a 3D telepresence, in which geographically separated users can interact with each other through their virtual avatars. In this paper, we present novel methods to determine the avatar's position in an indoor space to preserve the semantics of the user's position in a dissimilar indoor space with different space configurations and furniture layouts. To this end, we first perform a user survey on the preferred avatar placements for various indoor configurations and user placements, and identify a set of related attributes, including interpersonal relation, visual attention, pose, and spatial characteristics, and quantify these attributes with a set of features. By using the obtained dataset and identified features, we train a neural network that predicts the similarity between two placements. Next, we develop an avatar placement method that preserves the semantics of the placement of the remote user in a different space as much as possible. We show the effectiveness of our methods by implementing a prototype AR-based telepresence system and user evaluations.