ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models

Abstract

3D asset generation is getting massive amounts of attention inspired by the recent success on text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on synthetic data which often results in non-photorealistic 3D objects without backgrounds. In this paper we present a method that leverages pretrained text-to-image models as a prior and learn to generate multi-view images in a single denoising process from real-world data. Concretely we propose to integrate 3D volume-rendering and cross-frame-attention layers into each block of the existing U-Net network of the text-to-image model. Moreover we design an autoregressive generation that renders more 3D-consistent images at any viewpoint. We train our model on real-world datasets of objects and showcase its capabilities to generate instances with a variety of high-quality shapes and textures in authentic surroundings. Compared to the existing methods the results generated by our method are consistent and have favorable visual quality (-30% FID -37% KID).