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

翻译：本文提出了一种名为SyncDreamer的新型扩散模型，该模型能够从单视角图像生成多视角一致性图像。近期工作Zero123利用预训练的大规模二维扩散模型，展示了从物体单视角图像生成合理新视角的能力。然而，生成图像在几何与颜色上保持一致性仍具挑战。为解决这一问题，我们提出了一种同步多视角扩散模型，该模型对多视角图像的联合概率分布进行建模，从而在单一反向过程中生成多视角一致性图像。SyncDreamer通过一种三维感知特征注意力机制，在反向过程的每一步中同步所有生成图像的中间状态，该机制能够关联不同视角间的对应特征。实验表明，SyncDreamer生成的图像在不同视角间具有高度一致性，因此可广泛应用于新视角合成、文本到三维、图像到三维等多种三维生成任务。