  With recent developments in Embodied Artificial Intelligence (EAI) research,
there has been a growing demand for high-quality, large-scale interactive scene
generation. While prior methods in scene synthesis have prioritized the
naturalness and realism of the generated scenes, the physical plausibility and
interactivity of scenes have been largely left unexplored. To address this
disparity, we introduce PhyScene, a novel method dedicated to generating
interactive 3D scenes characterized by realistic layouts, articulated objects,
and rich physical interactivity tailored for embodied agents. Based on a
conditional diffusion model for capturing scene layouts, we devise novel
physics- and interactivity-based guidance mechanisms that integrate constraints
from object collision, room layout, and object reachability. Through extensive
experiments, we demonstrate that PhyScene effectively leverages these guidance
functions for physically interactable scene synthesis, outperforming existing
state-of-the-art scene synthesis methods by a large margin. Our findings
suggest that the scenes generated by PhyScene hold considerable potential for
facilitating diverse skill acquisition among agents within interactive
environments, thereby catalyzing further advancements in embodied AI research.
Project website: http://physcene.github.io.
