The field of cross-modal image synthesis is rapidly evolving, with a focus on developing innovative models that can generate high-quality images across different domains. Recent research has explored the integration of diffusion-based models and generative adversarial networks to improve the consistency and visual quality of generated images. Another significant direction is the development of efficient and consistent text-to-multi-view synthesis models, which can produce synthetic multi-view images from a text prompt in a matter of seconds. Environment-aware satellite image generation is also an area of growing interest, with models being conditioned on environmental context to generate images that reflect dynamic conditions. Furthermore, style-disentangled flow-based generative models are being proposed for RGB-to-thermal image translation, enabling the synthesis of thermal images from abundant RGB datasets. Notable papers in this area include: RapidMV, which introduces a novel spatio-angular latent space for efficient and consistent text-to-multi-view synthesis. ThermalGen, which proposes an adaptive flow-based generative model for RGB-T image translation, incorporating an RGB image conditioning architecture and a style-disentangled mechanism.