2 Dimensional Diffusion Model

Two-dimensional (2D) diffusion models, initially designed for image generation, are increasingly leveraged to advance 3D content creation. Current research focuses on adapting these pre-trained 2D models for 3D tasks, often employing techniques like Score Distillation Sampling (SDS) to guide the generation of 3D models from single images or multiple views, and incorporating multi-view consistency constraints to improve the quality and realism of the generated 3D assets. This approach offers a computationally efficient pathway to high-fidelity 3D generation, impacting fields such as virtual reality, game development, and medical imaging through improved reconstruction and synthesis capabilities.