Methods and Strategies for 3D Content Creation Based on 3D Native Methods

Abstract

The present paper provides a comprehensive overview of three neural network models, namely Point·E, 3DGen, and Shap·E, with a focus on their overall processes, network structures, loss functions, as well as their strengths, weaknesses, and potential future research opportunities. Point·E, an efficient framework, generates 3D point clouds from complex text prompts, leveraging a text-to-image diffusion model followed by 3D point cloud creation. 3DGen, a novel architecture, integrates a Variational Autoencoder with a diffusion model to produce triplane features for conditional and unconditional 3D object generation. Shap·E, a conditional generative model, directly generates parameters of implicit functions, enabling the creation of textured meshes and neural radiance fields. While these models demonstrate significant advancements in 3D generation, areas for improvement include enhancing sample quality, optimizing computational efficiency, and handling more complex scenes. Future research could explore further integration of these models with other techniques and extend their capabilities to address these challenges.