Remote telepresence via next-generation mixed reality platforms can provide
higher levels of immersion for computer-mediated communications, allowing
participants to engage in a wide spectrum of activities, previously not
possible in 2D screen-based communication methods. However, as mixed reality
experiences are limited to the local physical surrounding of each user, finding
a common virtual ground where users can freely move and interact with each
other is challenging. In this paper, we propose a novel mutual scene synthesis
method that takes the participants' spaces as input, and generates a virtual
synthetic scene that corresponds to the functional features of all
participants' local spaces. Our method combines a mutual function optimization
module with a deep-learning conditional scene augmentation process to generate
a scene mutually and physically accessible to all participants of a mixed
reality telepresence scenario. The synthesized scene can hold mutual walkable,
sittable and workable functions, all corresponding to physical objects in the
users' real environments. We perform experiments using the MatterPort3D dataset
and conduct comparative user studies to evaluate the effectiveness of our
system. Our results show that our proposed approach can be a promising research
direction for facilitating contextualized telepresence systems for
next-generation spatial computing platforms.

本文提出了一种新方法，通过深度学习条件场景增强过程和相互功能优化模块相结合，生成适用于所有混合现实远程实时视频会议场景的场景合成方法，以促进下一代空间计算平台上的情境化遥控系统的发展。