Portal de Periódicos da CAPES

Pose2Room: Understanding 3D Scenes from Human Activities

2022; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-031-19812-0_25

1611-3349

Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nießner,

Hand Gesture Recognition Systems

With wearable IMU sensors, one can estimate human poses from wearable devices without requiring visual input. In this work, we pose the question: Can we reason about object structure in real-world environments solely from human trajectory information? Crucially, we observe that human motion and interactions tend to give strong information about the objects in a scene – for instance a person sitting indicates the likely presence of a chair or sofa. To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment. P2R-Net models the probability distribution of object class as well as a deep Gaussian mixture model for object boxes, enabling sampling of multiple, diverse, likely modes of object configurations from an observed human trajectory. In our experiments we show that P2R-Net can effectively learn multi-modal distributions of likely objects for human motions, and produce a variety of plausible object structures of the environment, even without any visual information. The results demonstrate that P2R-Net consistently outperforms the baselines on the PROX dataset and the VirtualHome platform.