Challenges and Opportunities in 3D Content Generation

TL;DR

This paper reviews the current state, challenges, and future directions of 3D content generation using AI, highlighting the potential of diffusion models and the need for further research in this nascent field.

Contribution

It synthesizes recent research on 3D content generation with large-scale models and proposes solutions for leveraging diffusion models despite existing challenges.

Findings

Diffusion models show promise for high-fidelity 3D generation

Data scarcity and computational costs are key challenges

Future research directions are outlined for advancing 3D AIGC

Abstract

This paper explores the burgeoning field of 3D content generation within the landscape of Artificial Intelligence Generated Content (AIGC) and large-scale models. It investigates innovative methods like Text-to-3D and Image-to-3D, which translate text or images into 3D objects, reshaping our understanding of virtual and real-world simulations. Despite significant advancements in text and image generation, automatic 3D content generation remains nascent. This paper emphasizes the urgency for further research in this area. By leveraging pre-trained diffusion models, which have demonstrated prowess in high-fidelity image generation, this paper aims to summary 3D content creation, addressing challenges such as data scarcity and computational resource limitations. Additionally, this paper discusses the challenges and proposes solutions for using pre-trained diffusion models in 3D content generation. By synthesizing relevant research and outlining future directions, this study contributes to advancing the field of 3D content generation amidst the proliferation of large-scale AIGC models.