3D Shape Generation

3D shape generation aims to create realistic and diverse three-dimensional objects from various input modalities, such as text descriptions, sketches, or images. Current research heavily utilizes diffusion models, often coupled with transformer architectures or other efficient sequence modeling techniques, and explores diverse 3D representations like octrees, implicit neural representations (e.g., signed distance fields), and voxel grids to improve generation speed, quality, and controllability. This field is significant for its potential impact on various applications, including computer-aided design, virtual and augmented reality, and animation, by enabling efficient and intuitive 3D content creation.