3D Diffusion

3D diffusion models are rapidly advancing the field of 3D generative modeling, aiming to create high-quality, diverse three-dimensional content from various inputs, such as text or images. Current research focuses on improving efficiency and scalability through novel architectures like diffusion transformers and latent neural fields, often incorporating techniques such as structured radiance representations and multi-view approaches to overcome the challenges of high-dimensional data. These advancements are significantly impacting fields like medical imaging (e.g., improved CT reconstruction) and computer graphics (e.g., faster and more realistic 3D object generation), enabling more efficient and effective creation of complex 3D data.