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Challenges and Opportunities in 3D Content Generation

Ke Zhao, Andreas Larsen

TL;DR

3D content generation remains nascent relative to 2D, hindered by data scarcity and computational demands. The authors advocate leveraging pre-trained diffusion models to guide Text-to-3D and Image-to-3D, detailing how forward and reverse diffusion processes, along with diffusion priors, can synthesize 3D assets with reduced data needs. They review 3D representations (explicit and implicit) and representative methods (NeRF, 3D Gaussian Splatting) and synthesize approaches for Text-to-3D and Image-to-3D, including Diffusion-based DreamFusion and Zero-1-to-3. The work highlights applications across film, architecture, VR/AR, product design, and medical imaging, and outlines future directions to advance 3D content generation under large-scale AIGC models.

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.

Challenges and Opportunities in 3D Content Generation

TL;DR

3D content generation remains nascent relative to 2D, hindered by data scarcity and computational demands. The authors advocate leveraging pre-trained diffusion models to guide Text-to-3D and Image-to-3D, detailing how forward and reverse diffusion processes, along with diffusion priors, can synthesize 3D assets with reduced data needs. They review 3D representations (explicit and implicit) and representative methods (NeRF, 3D Gaussian Splatting) and synthesize approaches for Text-to-3D and Image-to-3D, including Diffusion-based DreamFusion and Zero-1-to-3. The work highlights applications across film, architecture, VR/AR, product design, and medical imaging, and outlines future directions to advance 3D content generation under large-scale AIGC models.

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.
Paper Structure (25 sections, 6 equations, 5 figures)

This paper contains 25 sections, 6 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. 3D Representation
  4. Explicit Representations
  5. Meshes
  6. Point Clouds
  7. Voxels
  8. Implicit Representations
  9. Neural Radiance Fields
  10. 3D Gaussian Splatting
  11. Diffusion models
  12. Forward Process (Diffusion)
  13. Reverse Process (Denoising)
  14. Training Objective
  15. Sampling
  16. ...and 10 more sections

Figures (5)

  • Figure 1: DreamFusion poole2022dreamfusion utilizes a pretrained text-to-image diffusion model to generate realistic 3D models from text prompts..
  • Figure 2: We demonstrate DreamFusion's capability to translate text into 3D assets.
  • Figure 3: We show the pipeline of Dreamfusion.
  • Figure 4: We show the pipeline of method Zero-1-to-3 liu2023zero.
  • Figure 5: Zero-1-to-3 provides a visual template for users to easily create desired 3D assets according to their specific needs.