Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models

Understand

Recent CLIP-guided 3D optimization methods, such as DreamFields and PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D synthesis.

• However, due to scratch training and random initialization without prior knowledge, these methods often fail to generate accurate and faithful 3D structures that conform to the input text.
• In this paper, we make the first attempt to introduce explicit 3D shape priors into the CLIP-guided 3D optimization process.
• Specifically, we first generate a high-quality 3D shape from the input text in the text-to-shape stage as a 3D shape prior.