Optimization of Multimodal Generative Models for Creative Content Generation
摘要
This research paper presents an in-depth examination of recent developments in multimodal generative models with a specific focus on enhancing creative content generation. We introduce a novel architectural framework that seamlessly integrates text, image, and audio modalities, enabling cross-modal translation of creative concepts. Extensive empirical evaluations demonstrate the model’s proficiency in generating creative content, characterized by high quality, coherence, and diversity. In our day-to-day life, the applications are massive which include digital and physical marketing and personalizing our choices in our day-to-day life.