The field of simulation and synthetic data generation is moving towards more comprehensive and systematic approaches. Recent developments have focused on creating high-quality synthetic data that mimics real-world dynamics, which is crucial for model development and robust assessment. Researchers are exploring novel methods to generate controllable, reasonable, and adaptable synthetic data for various applications, including market simulation, autonomous driving, and semantic segmentation. These advancements have the potential to significantly improve the performance and adaptability of downstream models, particularly in highly complex and volatile environments. Noteworthy papers include:

• Financial Wind Tunnel, which proposes a retrieval-augmented market simulator for generating controllable market dynamics.
• SceneCrafter, which introduces a realistic and efficient AD simulator for generating realistic driving logs.
• PDDM, which presents a pseudo depth diffusion model for RGB-PD semantic segmentation.
• Scenario Dreamer, which employs a novel vectorized latent diffusion model for generating driving simulation environments.