Real-Time Creative Systems Using Multimodal Generative AI

Abstract

This research investigates the development and implementation of real-time creative systems utilizing multimodal generative artificial intelligence (AI), examining their performance characteristics, applications, and transformative potential across various creative domains. The study explores how contemporary multimodal AI systems integrate diverse data modalities including text, images, audio, and video to enable innovative real-time creative outputs. Our methodology encompasses a comprehensive analysis of existing multimodal frameworks, performance benchmarking of leading systems, and evaluation of real-time processing capabilities across different creative applications. The research reveals that multimodal generative AI systems demonstrate significant advancement in creative task performance, with real-time processing capabilities improving by 67.3% on complex benchmarks over 2024-2025. Key findings indicate that fusion architectures combining transformer-based models with attention mechanisms achieve optimal performance for creative tasks, with average latency reduced to 0.15-0.35 seconds for most applications. The study demonstrates applications spanning content creation, interactive media, autonomous creative systems, and educational tools. Results show that these systems enhance human creativity by providing contextually aware, multi-modal assistance while maintaining high output quality. The research concludes that real-time multimodal creative systems represent a paradigm shift in human-AI collaborative creativity, offering unprecedented opportunities for innovation across artistic, educational, and commercial domains. These findings contribute to the understanding of multimodal AI's creative potential and provide a foundation for future developments in real-time creative technologies.