Novel 3D-Aware Composition Images Synthesis for Object Display with Diffusion Model

Designing attractive images for object display can be a time-consuming and skill-intensive process. The emergence of advanced algorithms, particularly the Diffusion Model, has made it possible to synthesize attractive images using AI. However, the existing diffusion models are mostly used to generate entire images and lack control over specific objects for object display. Here, to the best of our knowledge, we pioneers to extend the application of the diffusion model to synthesize novel images for specific objects. By encoding the input images of objects into NeRF representation and synthesizing the desired backgrounds using diffusion models with the input of rendered object images and text prompts, our method can generate 3D aware object display images at arbitrary angles and arbitrary backgrounds. We have conducted extensive experiments to demonstrate that our method is capable of generating high-quality and photo-realistic images, which are > 6 times faster than the conventional photomontage approach. Moreover, our generated images have higher compositional scores, image quality scores, and aesthetics scores in our user experiments. By significantly reducing the need for human effort and producing higher quality generated images, our approach opens up exciting possibilities for creating versatile novel images of specific objects.