ViewFusion：通过插值去噪实现多视角一致性

摘要

通过扩散模型进行新视角合成已经展现出生成多样且高质量图像的显著潜力。然而，在这些主流方法中图像生成的独立过程导致了在保持多视角一致性方面的挑战。为了解决这个问题，我们引入了ViewFusion，这是一种新颖的、无需训练的算法，可以无缝地集成到现有预训练的扩散模型中。我们的方法采用自回归方法，隐式地利用先前生成的视角作为下一个视角生成的上下文，确保在新视角生成过程中具有强大的多视角一致性。通过一个融合已知视角信息的扩散过程，通过插值去噪，我们的框架成功地将单视角条件模型扩展到多视角条件设置中，而无需进行额外的微调。大量的实验结果展示了ViewFusion在生成一致且详细的新视角方面的有效性。

English

Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet, the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this, we introduce ViewFusion, a novel, training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation, ensuring robust multi-view consistency during the novel-view generation process. Through a diffusion process that fuses known-view information via interpolated denoising, our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning. Extensive experimental results demonstrate the effectiveness of ViewFusion in generating consistent and detailed novel views.

ViewFusion：通过插值去噪实现多视角一致性

ViewFusion: Towards Multi-View Consistency via Interpolated Denoising

摘要

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