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Recent CLIP-guided 3D optimization methods, such as DreamFields andPureCLIPNeRF, have achieved impressive results in zero-shot text-to-3Dsynthesis. However, due to scratch training and random initialization withoutprior knowledge, these methods often fail to generate accurate and faithful 3Dstructures that conform to the input text. In this paper, we make the firstattempt to introduce explicit 3D shape priors into the CLIP-guided 3Doptimization process. Specifically, we first generate a high-quality 3D shapefrom the input text in the text-to-shape stage as a 3D shape prior. We then useit as the initialization of a neural radiance field and optimize it with thefull prompt. To address the challenging text-to-shape generation task, wepresent a simple yet effective approach that directly bridges the text andimage modalities with a powerful text-to-image diffusion model. To narrow thestyle domain gap between the images synthesized by the text-to-image diffusionmodel and shape renderings used to train the image-to-shape generator, wefurther propose to jointly optimize a learnable text prompt and fine-tune thetext-to-image diffusion model for rendering-style image generation. Our method,Dream3D, is capable of generating imaginative 3D content with superior visualquality and shape accuracy compared to state-of-the-art methods.

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