Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation
CoRR(2024)
Abstract
Using image as prompts for 3D generation demonstrate particularly strong
performances compared to using text prompts alone, for images provide a more
intuitive guidance for the 3D generation process. In this work, we delve into
the potential of using multiple image prompts, instead of a single image
prompt, for 3D generation. Specifically, we build on ImageDream, a novel
image-prompt multi-view diffusion model, to support multi-view images as the
input prompt. Our method, dubbed MultiImageDream, reveals that transitioning
from a single-image prompt to multiple-image prompts enhances the performance
of multi-view and 3D object generation according to various quantitative
evaluation metrics and qualitative assessments. This advancement is achieved
without the necessity of fine-tuning the pre-trained ImageDream multi-view
diffusion model.
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