Advanced 3D Generation Techniques

Set up a framework for single-view 3D reconstruction by optimizing a NeRF conditioned on an input image

Apply a pre-trained diffusion model as a prior to guide single-view NeRF optimization

Apply diffusion priors for 3D scene completion from partial data

Explain how camera pose information is encoded and used as a conditioning signal in 3D generative models

Integrate camera conditioning into a 3D diffusion model to generate novel views of a scene

Apply diffusion models for object placement and spatial arrangement in 3D scenes

Implement mesh extraction from a volumetric representation using marching cubes

Debug multi-view consistency and view-dependent artifacts