The recent advancements in the field of computer vision and generative modeling have shown a significant shift towards enhancing the realism and controllability of synthetic images. Researchers are increasingly focusing on integrating domain-specific knowledge with powerful generative models like diffusion models and GANs to achieve more accurate and editable image translations. This trend is particularly evident in the areas of rendered-to-real image translation and 3D face reconstruction, where the goal is to bridge the gap between synthetic and real images while preserving fine details and textures. Additionally, there is a growing interest in developing object-centric learning methods that can identify and generate objects across various scenes, akin to human perception. These methods aim to disentangle scene-dependent attributes from globally invariant object representations, enabling more robust and versatile AI systems. Overall, the field is moving towards more sophisticated and controllable generative models that can better mimic human-like perception and creativity in image synthesis.