ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models
Lukas Höllein Aljaž Božič Norman Müller David Novotny Hung-Yu Tseng Christian Richardt Michael Zollhöfer Matthias Nießner
3D asset generation is getting massive amounts of attention, inspired by the
recent success of 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).