Placement Retargeting of Virtual Avatars to Dissimilar Indoor Environments
This work is significant for users of AR-based telepresence systems, as it aims to improve the naturalness and semantic consistency of avatar interactions in virtual spaces, especially when real and virtual environments differ.
This paper addresses the challenge of placing a virtual avatar in a dissimilar indoor environment while preserving the semantic meaning of the user's real-world position. They developed a neural network that predicts placement similarity based on user survey data and identified attributes, then used this to guide avatar placement in a prototype AR telepresence system.
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.