  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.
