In this paper, we introduce a novel 3D-aware image generation method that
leverages 2D diffusion models. We formulate the 3D-aware image generation task
as multiview 2D image set generation, and further to a sequential
unconditional-conditional multiview image generation process. This allows us to
utilize 2D diffusion models to boost the generative modeling power of the
method. Additionally, we incorporate depth information from monocular depth
estimators to construct the training data for the conditional diffusion model
using only still images. We train our method on a large-scale dataset, i.e.,
ImageNet, which is not addressed by previous methods. It produces high-quality
images that significantly outperform prior methods. Furthermore, our approach
showcases its capability to generate instances with large view angles, even
though the training images are diverse and unaligned, gathered from
"in-the-wild" real-world environments.Comment: Website: https://jeffreyxiang.github.io/ivid