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Humans naturally change their environment through interactions, e.g., byopening doors or moving furniture. To reproduce such interactions in virtualspaces (e.g., metaverse), we need to capture and model them, including changesin the scene geometry, ideally from egocentric input alone (head camera andbody-worn inertial sensors). While the head camera can be used to localize theperson in the scene, estimating dynamic object pose is much more challenging.As the object is often not visible from the head camera (e.g., a human notlooking at a chair while sitting down), we can not rely on visual object poseestimation. Instead, our key observation is that human motion tells us a lotabout scene changes. Motivated by this, we present iReplica, the firsthuman-object interaction reasoning method which can track objects and scenechanges based solely on human motion. iReplica is an essential first steptowards advanced AR/VR applications in immersive virtual universes and canprovide human-centric training data to teach machines to interact with theirsurroundings. Our code, data and model will be available on our project page athttp://virtualhumans.mpi-inf.mpg.de/ireplica/

Interaction Replica: Tracking human-object interaction and scene changes from human motion