Generative models for text and images have gained widespread popularity thanks to their remarkable advancements in producing highly realistic outputs. However, the challenge of generating 3D content remains largely unresolved, primarily due to the limited availability of large-scale data. Nevertheless, recent attempts to generate 3D content using 2D priors have shown surprising success, albeit with some instances of failure.

In this workshop, we will discuss recent advances in 3D generative models, with a focus on the fundamental components within the 3D generative model pipelines: NeRF, Diffusion Models, and Score Distillation Loss. Additionally, we will address the limitations of current pipelines and outline potential future research directions aimed at overcoming these challenges.