DynamicTHOR: A Scalable Dataset of Human-Centric Dynamic Scenes for Embodied AI

Although many existing works have studied probabilistic or dynamic environments where the objects used in daily life may be moved due to human activities, the scale of datasets is usually limited due to the cost of human annotation or manual configuration. This paper introduces a framework that simulates human activities and corresponding object dynamics using Large Language Models (LLMs) and applies the simulated human residents to embodied scenes to generate dynamic scenes. Using this framework, we craft a dataset named DynamicTHOR with 50 characters and 100 dynamic scenes, which can be easily extended in scale. A user study comparing our generated scene dynamics with a baseline approach and human annotations validates that our framework successfully produces believable, diversified data of a quality comparable to human annotations. The novel framework and dataset can facilitate the study of embodied intelligence, such as the navigation task in dynamic scenarios.