3D Object Generation

3D object generation aims to create realistic three-dimensional models from various inputs, such as text descriptions or single images. Current research heavily utilizes diffusion models, often incorporating techniques like score distillation sampling and variational distribution mapping, to generate high-fidelity outputs, with a growing emphasis on controllable multi-object scenes and efficient generation methods. This field is significant for its potential applications in virtual and augmented reality, robotics, and industrial design, driving advancements in both generative AI and 3D computer vision.