Guiding 3D Digital Content Generation with Pre-Trained Diffusion Models

The production technology of 3D digital content involves multiple stages, including 3D modeling, simulation animation, visualization rendering, and perceptual interaction. It is not only the core technology supporting the creation of 3D digital content but also a key element in enhancing immersive application experiences in virtual reality and the metaverse. A primary focus in computer vision and computer graphics research has been on how to create 3D digital content that is efficient, convenient, controllable, and editable. Currently, producing high-quality 3D digital content still requires significant time and effort from a large number of designers. To address this challenge, leveraging artificial intelligence-generated methods to break down production barriers has emerged as an effective strategy. With the substantial breakthroughs achieved by diffusion models in the field of image generation, they also demonstrate tremendous potential in 3D digital content generation, potentially becoming a foundational model in this area. Recent studies have shown that diffusion model-based techniques for generating 3D digital content can significantly reduce production costs and enhance efficiency. Therefore, it is essential to summarize and categorize existing methods to facilitate further research. This paper systematically reviews 3D digital content generation methods, introducing related 3D representation techniques and focusing on 3D digital content generation schemes, algorithms, and pipeline based on diffusion models. We perform a horizontal comparison of different approaches in terms of generation speed and quality, deeply analyze existing challenges, and propose viable solutions. Furthermore, we thoroughly explore future research themes and directions in this domain, aiming to provide guidance and reference for subsequent research endeavors.

@article{Li2024,
  title     = {Guiding 3D Digital Content Generation with Pre-Trained Diffusion Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jing Li and Zhengping Li and Peizhe Jiang and Lijun Wang and Xiaoxue Li and Yuwen Hao},
  doi       = {10.14569/IJACSA.2024.01501120},
  url       = {https://doi.org/10.14569/IJACSA.2024.01501120}
}