Text to 3D

Text-to-3D generation aims to create three-dimensional models from textual descriptions, automating a traditionally laborious process. Current research heavily utilizes pre-trained 2D image diffusion models, adapting their capabilities for 3D generation through techniques like score distillation sampling and incorporating multi-view consistency into model architectures such as NeRFs and Gaussian splatting. This field is significant for its potential to revolutionize 3D content creation across diverse applications, from gaming and virtual reality to robotics and industrial design, by offering faster, more accessible, and potentially more creative design workflows.