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This work proposes a novel method called DiffMove that harnesses crowd wisdom for trajectory recovery and integrates group mobility tendencies and individual preferences into the spatiotemporal distribution of the trajectory to recover high-quality trajectories.
This work proposes Real-time Adaptive Physics-Informed Diffusion (RAPID), a unified framework explicitly designed to balance high-fidelity generation with strict real-time constraints, and establishes a new state-of-the-art balance between fidelity and safety.
Generating realistic and diverse pedestrian background flows is critical for numerous downstream applications, ranging from the training and validation of autonomous driving systems to the simulation of mobile communication networks. While recent diffusion-based models achieve state-of-the-art accuracy, they suffer fro...
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