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TransforMR: Pose-Aware Object Substitution for Composing Alternate Mixed Realities

Mohamed Kari, Tobias Grosse-Puppendahl, Luis Falconeri Coelho, Andreas Fender, David Bethge, Reinhard Schütte, and Christian Holz. Proceedings of IEEE ISMAR 2021.
Abstract

Despite the advances in machine perception, semantic scene understanding is still a limiting factor in mixed reality scene composition. In this paper, we present TransforMR, a video see-through mixed reality system for mobile devices that performs 3D-pose-aware object substitution to create meaningful mixed reality scenes. In real-time and for previously unseen and unprepared real-world environments, TransforMR composes mixed reality scenes so that virtual objects assume behavioral and environment-contextual properties of replaced real-world objects. This yields meaningful, coherent, and human-interpretable scenes, not yet demonstrated by today’s augmentation techniques. TransforMR creates these experiences through our novel pose-aware object substitution method building on different 3D object pose estimators, instance segmentation, video inpainting, and pose-aware object rendering. TransforMR is designed for use in the real-world, supporting the substitution of humans and vehicles in everyday scenes, and runs on mobile devices using just their monocular RGB camera feed as input. We evaluated TransforMR with eight participants in an uncontrolled city environment employing different transformation themes. Applications of TransforMR include real-time character animation analogous to motion capturing in professional film making, however without the need for preparation of either the scene or the actor, as well as narrative-driven experiences that allow users to explore fictional parallel universes in mixed reality.