AI 3D Generation, explained

A comprehensive overview of recent progress in AI-driven 3D content generation. Covers how 2D diffusion models are leveraged for 3D generation via Score Distillation Sampling (SDS/DreamFusion), improvements for higher resolution and faster training using Gaussian Splatting, single-image 3D reconstruction techniques, and the use of 3D datasets to train view-conditional diffusion models. Also explains variational score distillation, multi-view prediction, and Transformer-based approaches that directly predict 3D representations without per-instance optimization, achieving results in seconds.