[論文レビュー] Beyond Reality: The Pivotal Role of Generative AI in the Metaverse

Vinay Chamola, Gaurang Bansal+9 more|arXiv (Cornell University)|Jul 28, 2023

Artificial Intelligence in Healthcare and Education被引用数 11

ひとこと要約

この論文は、生成系AIモデルがテキスト、画像、動画、3D領域を横断してメタバースコンテンツを可能にする方法を概説し、応用と倫理を論じ、アバター生成に関するケーススタディを提示します。

ABSTRACT

Imagine stepping into a virtual world that's as rich, dynamic, and interactive as our physical one. This is the promise of the Metaverse, and it's being brought to life by the transformative power of Generative Artificial Intelligence (AI). This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world. We delve into the applications of text generation models like ChatGPT and GPT-3, which are enhancing conversational interfaces with AI-generated characters. We explore the role of image generation models such as DALL-E and MidJourney in creating visually stunning and diverse content. We also examine the potential of 3D model generation technologies like Point-E and Lumirithmic in creating realistic virtual objects that enrich the Metaverse experience. But the journey doesn't stop there. We also address the challenges and ethical considerations of implementing these technologies in the Metaverse, offering insights into the balance between user control and AI automation. This paper is not just a study, but a guide to the future of the Metaverse, offering readers a roadmap to harnessing the power of generative AI in creating immersive virtual worlds.

研究の動機と目的

• Motivate the study of the Metaverse as a fusion of virtual and physical reality and the need for AI-driven content creation.
• Identify how generative AI can enhance immersion, personalization, and interactivity in Metaverse domains (text, image, video, 3D objects).
• Provide a taxonomy of generative AI models (VAEs, GANs, Transformers, autoregressive models) and map them to Metaverse applications.
• Highlight challenges, ethical considerations, and future directions for responsible deployment of generative AI in the Metaverse.

提案手法

• Classify Metaverse content into four generation domains: text, image, video, and 3D objects.
• Categorize generative models into four families: VAEs, GANs, Transformers, and autoregressive models.
• Present a domain-model mapping with examples (Table I) to illustrate applicability.
• Discuss applications, workflows, and a case study to demonstrate a practical avatar-generation pipeline.

(a) Domains in world of metaverse

実験結果

リサーチクエスチョン

• RQ1 How do VAEs, GANs, Transformers, and autoregressive models map onto text, image, video, and 3D object generation in the Metaverse?
• RQ2 What are the key applications and workflows in each generation domain for immersive Metaverse experiences?
• RQ3 What open issues, challenges, and ethical considerations arise in deploying generative AI in the Metaverse, and what future directions are proposed?
• RQ4 How does a practical avatar-generation case study illustrate the integration of perception, prompting, and diffusion-based generation?
• RQ5 What are the main limitations and opportunities for interoperability and efficiency in generative AI for the Metaverse?

主な発見

• Generative AI enables domain-specific content creation in text, image, video, and 3D object generation for the Metaverse.
• A structured mapping of models to domains highlights where VAEs, GANs, Transformers, and autoregressive models are most effective.
• The paper presents a case study showing diffusion models can translate user skeletons, derived from OpenPose, into plausible avatars with varying prompts and seeds.
• Open issues include data quality, realism, content control, ethics, computational efficiency, and interoperability, guiding future research directions.

(b) Divisions highlighted by cubes in Metaverse.