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In this paper, we introduce a novel 3D-aware image generation method thatleverages 2D diffusion models. We formulate the 3D-aware image generation taskas multiview 2D image set generation, and further to a sequentialunconditional-conditional multiview image generation process. This allows us toutilize 2D diffusion models to boost the generative modeling power of themethod. Additionally, we incorporate depth information from monocular depthestimators to construct the training data for the conditional diffusion modelusing 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-qualityimages that significantly outperform prior methods. Furthermore, our approachshowcases its capability to generate instances with large view angles, eventhough the training images are diverse and unaligned, gathered from"in-the-wild" real-world environments.

3D-aware Image Generation using 2D Diffusion Models