Physically-Based Simulation for Generative AI Models

Given the vast development of generative AI models spanning various domains and industries, the need for high-quality, diverse, and extensive datasets has become increasingly pronounced. However, the procurement of such datasets is often fraught with challenges, particularly in fields where data is either scarce, expensive, or sensitive.

In response to this challenge, synthetic data generation coupled with the integration of physical priors has emerged as a potent solution to bridge the existing gap. This approach offers the capability to simulate and embed data that closely mimics real-world scenarios, providing a valuable resource for training and enhancing the performance of generative models.

In this talk, we will delve into the multifaceted capabilities of physically-based simulation within the realm of generative models. Furthermore, we will explore the far-reaching impact of this approach across diverse domains, encompassing applications such as photo editing, navigation systems, digital human representation, and even unraveling the mysteries of the Big Bang.

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