  This paper introduces MVDiffusion, a simple yet effective method for
generating consistent multi-view images from text prompts given pixel-to-pixel
correspondences (e.g., perspective crops from a panorama or multi-view images
given depth maps and poses). Unlike prior methods that rely on iterative image
warping and inpainting, MVDiffusion simultaneously generates all images with a
global awareness, effectively addressing the prevalent error accumulation
issue. At its core, MVDiffusion processes perspective images in parallel with a
pre-trained text-to-image diffusion model, while integrating novel
correspondence-aware attention layers to facilitate cross-view interactions.
For panorama generation, while only trained with 10k panoramas, MVDiffusion is
able to generate high-resolution photorealistic images for arbitrary texts or
extrapolate one perspective image to a 360-degree view. For multi-view
depth-to-image generation, MVDiffusion demonstrates state-of-the-art
performance for texturing a scene mesh.
