Shape Generation

Shape generation, the automated creation of three-dimensional shapes, aims to improve efficiency and creativity in fields like computer-aided design and virtual reality. Current research heavily utilizes deep learning models, particularly diffusion models and transformers, often incorporating implicit surface representations like signed distance functions or point clouds, and leveraging various conditioning inputs such as text, images, and sketches to control the generation process. These advancements enable high-fidelity shape creation with diverse topologies and improved control, impacting fields ranging from product design to drug discovery through more efficient and creative 3D content creation.