Gaussian Flow

Gaussian flows are a class of generative models leveraging continuous transformations of probability distributions, primarily Gaussian distributions, to generate diverse and complex data. Current research focuses on applying Gaussian flows to various domains, including audio processing, 4D content creation, and trajectory prediction, often employing architectures like Gaussian splatting and mixed Gaussian models to improve data representation and controllability. These methods offer advantages in handling high-dimensional data, uncertainty quantification, and generating samples with specific properties, impacting fields like computer vision, robotics, and scientific data analysis. The ability to model complex dynamics and uncertainties makes Gaussian flows a powerful tool for diverse applications.