MV-RAG: Retrieval Augmented Multiview Diffusion
Authors: name: Yosef Dayani, name: Omer Benishu, name: Sagie Benaim

Text-to-3D generation approaches have advanced significantly by leveraging
pretrained 2D diffusion priors, producing high-quality and 3D-consistent
outputs. However, they often fail to produce out-of-domain (OOD) or rare
concepts, yielding inconsistent or inaccurate results. To this end, we propose
MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images
from a large in-the-wild 2D database and then conditions a multiview diffusion
model on these images to synthesize consistent and accurate multiview outputs.
Training such a retrieval-conditioned model is achieved via a novel hybrid
strategy bridging structured multiview data and diverse 2D image collections.
This involves training on multiview data using augmented conditioning views
that simulate retrieval variance for view-specific reconstruction, alongside
training on sets of retrieved real-world 2D images using a distinctive held-out
view prediction objective: the model predicts the held-out view from the other
views to infer 3D consistency from 2D data. To facilitate a rigorous OOD
evaluation, we introduce a new collection of challenging OOD prompts.
Experiments against state-of-the-art text-to-3D, image-to-3D, and
personalization baselines show that our approach significantly improves 3D
consistency, photorealism, and text adherence for OOD/rare concepts, while
maintaining competitive performance on standard benchmarks.