ViewFusion: Towards Multi-View Consistency via Interpolated Denoising

• URL: http://arxiv.org/abs/2402.18842v1
• Date: Thu, 29 Feb 2024 04:21:38 GMT
• Title: ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
• Authors: Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, Anton van den Hengel
• Abstract summary: We introduce ViewFusion, a 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. Our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning.
• Score: 48.02829400913904
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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