  Text-guided diffusion models have shown superior performance in image/video
generation and editing. While few explorations have been performed in 3D
scenarios. In this paper, we discuss three fundamental and interesting problems
on this topic. First, we equip text-guided diffusion models to achieve
3D-consistent generation. Specifically, we integrate a NeRF-like neural field
to generate low-resolution coarse results for a given camera view. Such results
can provide 3D priors as condition information for the following diffusion
process. During denoising diffusion, we further enhance the 3D consistency by
modeling cross-view correspondences with a novel two-stream (corresponding to
two different views) asynchronous diffusion process. Second, we study 3D local
editing and propose a two-step solution that can generate 360-degree
manipulated results by editing an object from a single view. Step 1, we propose
to perform 2D local editing by blending the predicted noises. Step 2, we
conduct a noise-to-text inversion process that maps 2D blended noises into the
view-independent text embedding space. Once the corresponding text embedding is
obtained, 360-degree images can be generated. Last but not least, we extend our
model to perform one-shot novel view synthesis by fine-tuning on a single
image, firstly showing the potential of leveraging text guidance for novel view
synthesis. Extensive experiments and various applications show the prowess of
our 3DDesigner. The project page is available at
https://3ddesigner-diffusion.github.io/.
