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.
@article{arxiv.2404.17419,
title = {Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation},
author = {Seungwook Kim and Yichun Shi and Kejie Li and Minsu Cho and Peng Wang},
journal= {arXiv preprint arXiv:2404.17419},
year = {2024}
}