Text to Video Generation Using Generative AI for Interior Design Visualization

The emerging discipline of text-to-video synthesis combines computer vision and natural language understanding to create coherent, realistic videos that are based on written descriptions. The research is an endeavour to provide a bridge between the fields of computer vision and natural language processing by using a robust text-to-video production system. The system's main goal is to convert text prompts into visually appealing videos using pre-trained models and style transfer techniques, providing a fresh approach to content development. The method demonstrates flexibility and effectiveness by including well-known libraries like PyTorch , PyTorch Lightning, and OpenCV. The work emphasises the potential of style transfer in boosting the creative quality of visual outputs by emphasising its capability to make videos with distinct styles through rigorous experimentation. The outcomes illustrate how language clues and artistic aesthetics can be successfully combined, as well as the system's ramifications for media production, entertainment, and communication. This study adds to the rapidly changing field of text-to-video synthesis and exemplifies the fascinating opportunities that result from the fusion of artificial intelligence and the production of multimedia content. Key Words: