Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a specified cost function. OT theory has been widely utilized in generative modeling. Initially, the OT-based Wasserstein metric served as a measure for assessing the distance between data and generated distributions. More recently, the OT transport map, connecting data and prior distributions, has emerged as a new approach for generative models. In this talk, we will introduce generative models based on Optimal Transport. Specifically, we will present our work on a generative model utilizing Unbalanced Optimal Transport. We will also discuss our subsequent efforts to address the challenges associated with this approach.