The recent advancements in style transfer and 3D object generation have seen a shift towards leveraging diffusion models and geometric representations to enhance the quality and flexibility of outputs. Diffusion-based methods, such as Z-STAR+ and LumiNet, are pioneering zero-shot style transfer and scene relighting by integrating generative priors directly into content images without retraining, thereby achieving more natural and artifact-free results. These approaches also demonstrate the potential to handle complex lighting phenomena and spatial layouts effectively. On the other hand, geometric-based techniques like GIST are pushing the boundaries of photorealism by capturing fine-grained details and global structures through multiscale representations, which outperform traditional methods in both quality and efficiency. Additionally, the introduction of multi-instance diffusion models like MIDI is revolutionizing single image to 3D scene generation by enabling the simultaneous creation of multiple 3D instances with precise spatial relationships, a significant leap from previous reconstruction or retrieval-based methods. Notably, Style3D stands out for its innovative use of attention-guided multi-view style transfer, which ensures both spatial coherence and stylistic fidelity in 3D object generation, offering a scalable solution that surpasses existing techniques in computational efficiency and visual quality. These developments collectively indicate a promising future where style transfer and 3D generation are not only more photorealistic but also more adaptable and efficient.