The rapid advancements in large language models (LLMs) have sparked a growing interest in integrating them with multimodal learning. While previous surveys of multimodal large language models (MLLMs) primarily focused on understanding, this survey provides an in-depth exploration of multimodal generation across various domains, including image, video, 3D, and audio. The survey highlights notable achievements and milestones in these fields, offering a comprehensive overview of the current state of multimodal generation.
The survey thoroughly examines the key technical components underlying the methods employed in these studies, as well as the multimodal datasets utilized. By providing a detailed analysis of these essential elements, the survey enables readers to gain a deeper understanding of the techniques and resources driving the progress in multimodal generation. Moreover, the survey explores the domain of tool-augmented multimodal agents, which leverage existing generative models to facilitate human-computer interaction. This investigation sheds light on the potential of these agents to revolutionize the way humans interact with AI systems, opening up new possibilities for seamless and intuitive communication.
In addition to the technical aspects, the survey also addresses the critical topic of AI safety in the context of multimodal generation. It provides a comprehensive discussion of the advancements made in ensuring the responsible development and deployment of these technologies. By emphasizing the importance of AI safety, the survey contributes to the ongoing discourse on the ethical considerations surrounding multimodal generation.
Lastly, the survey investigates the emerging applications of multimodal generation and presents insights into future prospects. This forward-looking perspective helps researchers and practitioners identify potential areas for further research and development, driving the field towards new horizons.
In summary, this survey offers a systematic and insightful overview of multimodal generation with large language models. By encompassing various domains, examining key technical components, exploring tool-augmented multimodal agents, addressing AI safety, and discussing emerging applications and future prospects, the survey serves as a valuable resource for researchers, practitioners, and enthusiasts interested in the advancement of Artificial Intelligence for Generative Content (AIGC) and world models.
