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Recent advancements in text-driven 3D content generation highlight several challenges. Surveys show that users often provide simple text inputs while expecting high-quality results. Generating optimal 3D content from minimal prompts is difficult due to the strong dependency of text-to-3D models on input quality. Moreover, the generation process exhibits high variability, often requiring many attempts to meet user expectations, reducing efficiency. To address this, we propose GPT-4V for self-optimization, enhancing generation efficiency and enabling satisfactory results in a single attempt.