Text-to-3D Object Generation

Explain the core mechanism of Score Distillation Sampling (SDS) for guiding a 3D representation with a 2D diffusion model

Implement the rendering step to generate a 2D view from a 3D representation (e.g., NeRF) at a random camera angle

Implement the SDS loss function for a single view, using a pre-trained 2D text-to-image diffusion model

Combine the components into a training loop to optimize a 3D representation from a text prompt

Debug 3D artifacts like the Janus problem (multi-headedness) and geometric inconsistencies

Apply diffusion models to generate explicit 3D representations like point clouds or voxels