The accurate representation of soil particle morphology is crucial for understanding its granular characteristics and assembly responses. However, incorporating realistic and diverse particle morphologies into modeling presents challenges, often requiring time-consuming and expensive X-ray Computed Tomography (XRCT). This has resulted in a prevalent issues in modeling: morphological particle generation. On this topic, we introduce the Metaball Variational Autoencoder. This method leverages deep neural networks to generate new 3D particles in the form of Metaballs while preserving essential morphological features from the parental particles. Furthermore, the method allows for shape control through an arithmetic pattern, enabling the generation of particles with specific shapes. We validate the generation fidelity by comparing the morphologies and shape-feature distributions of the generated particles with the parental data. Additionally, we provide examples to demonstrate the controllability of the generated shapes. By integrating these methods into the Metaball-based simulation framework proposed by the authors previously, we enable the incorporation of real particle shapes into simulations. This could facilitate the simulation of a large number of soil particles with varying shapes and behaviors, providing valuable insights into the properties and behavior of actual soil particles.