Text-To-Animation: Generating 3D Animation Using Textual Descriptions

Abstract: The quick progress in generative AI Opened new doors in 3D animation by adding allowing automated animation generation from text description. This paper presents Text to Animation (TTA), a new system that fine-tunes the DeepSeekCoder-5.7BMQA-Base model to convert natural language inputs into Blender Python Scripts. By incorporating Chainlit, the system provides real-time script generation and execution inside Blender with minimal human effort and improved user feedback. For performance rendering, animations are rendered on the cloud, maximizing computational effectiveness. The AIbased solution streamlines the animation pipeline for artists, teachers and creators by automating Blender scripting, hence reducing the technical barrier to producing high-quality 3D animations. It opens 3D content creation to a broader audience by minimizing the necessity of advanced technical skills. The model is fine-tuned with the DeepSeek-Coder-7.7BMQA-Base model with LoRA (Low-Rank Adaptation) for optimizing performance on tasks of generating Blender scripts. Possible users vary from training modules and educational simulation to gaming and cinematic previsualization. This article emphasizes how large language models transform creative industries by closing the gap between AI and 3D animation.Future research will concentrate on enhancing the model’s comprehension of intricate motion dynamics and user interaction during the animation process.