Simulation models for perception sensors are integral components of automotive simulators used for the virtual validation of autonomous driving systems (ADSs). They also function as powerful tools for generating synthetic datasets to train deep learning-based perception models. Light detection and ranging (LiDAR) is a widely adopted perception sensor in ADS due to its high precision in 3-D environment scanning. However, developing realistic LiDAR simulation models is a significant technical challenge. Unrealistic simulations can result in a large gap between the synthesized and real-world point clouds, limiting their effectiveness in perception and planning modules. Recently, deep generative models have emerged as promising solutions to synthesize realistic sensory data. However, for LiDAR simulation, deep generative models have been primarily hybridized with conventional algorithms, leaving unified generative approaches largely unexplored in the literature. Addressing this gap, we propose a unified generative framework to enhance LiDAR simulation fidelity. Our proposed framework projects LiDAR point clouds into depth-reflectance images via a lossless transformation, and employs our novel controllable LiDAR point cloud generative model, CoLiGen, to translate the images. We extensively evaluate our CoLiGen model, comparing it with the state-of-the-art (SOTA) image-to-image translation models using various metrics to assess the realness, faithfulness, and performance of a downstream perception model. Our results show that CoLi-Gen exhibits superior performance across most metrics. The dataset and source code for this research are available at https://github.com/hamedhaghighi/CoLiGen.git