The recent advancements in generative models have significantly pushed the boundaries of 3D object generation and graphic design. A notable trend is the development of unified frameworks capable of generating 3D objects from diverse input modalities, such as text, images, and audio, addressing the limitations of previous models that were restricted to specific tasks and modalities. These new approaches leverage cross-modal alignment techniques and innovative loss functions to enhance the alignment and quality of generated 3D objects. Additionally, there is a growing focus on improving the editability and diversity of text-guided scalable vector graphics (SVG), with methods that dynamically adjust vector primitives and incorporate advanced score distillation techniques to enhance visual quality and diversity. Furthermore, the integration of reference image prompts in text-to-3D generation models has shown to stabilize optimization processes and improve output quality, addressing the over-smoothing issues prevalent in existing methods. Overall, these developments indicate a shift towards more versatile, high-quality, and controllable generative models in both 3D object creation and graphic design.