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In this paper, we present a novel diffusion model called that generatesmultiview-consistent images from a single-view image. Using pretrainedlarge-scale 2D diffusion models, recent work Zero123 demonstrates the abilityto generate plausible novel views from a single-view image of an object.However, maintaining consistency in geometry and colors for the generatedimages remains a challenge. To address this issue, we propose a synchronizedmultiview diffusion model that models the joint probability distribution ofmultiview images, enabling the generation of multiview-consistent images in asingle reverse process. SyncDreamer synchronizes the intermediate states of allthe generated images at every step of the reverse process through a 3D-awarefeature attention mechanism that correlates the corresponding features acrossdifferent views. Experiments show that SyncDreamer generates images with highconsistency across different views, thus making it well-suited for various 3Dgeneration tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.

SyncDreamer: Generating Multiview-consistent Images from a Single-view Image