Text to 3D Generation

Text-to-3D generation aims to create three-dimensional models from textual descriptions, bridging the gap between natural language and 3D content creation. Current research heavily utilizes diffusion models, often coupled with techniques like Score Distillation Sampling (SDS) and Gaussian splatting, to generate high-fidelity 3D objects represented as neural radiance fields or meshes. These advancements are improving the realism, detail, and efficiency of 3D model generation, impacting fields such as computer graphics, animation, and virtual/augmented reality by offering faster and more intuitive content creation pipelines. Ongoing efforts focus on addressing challenges like geometric consistency, view consistency, and efficient generation of complex scenes.