MultiDiff: Consistent Novel View Synthesis from a Single Image

• URL: http://arxiv.org/abs/2406.18524v1
• Date: Wed, 26 Jun 2024 17:53:51 GMT
• Title: MultiDiff: Consistent Novel View Synthesis from a Single Image
• Authors: Norman Müller, Katja Schwarz, Barbara Roessle, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, Peter Kontschieder,
• Abstract summary: MultiDiff is a novel approach for consistent novel view synthesis of scenes from a single RGB image. Our results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging, real-world datasets RealEstate10K and ScanNet.
• Score: 60.04215655745264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce MultiDiff, a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image is highly ill-posed by nature, as there exist multiple, plausible explanations for unobserved areas. To address this issue, we incorporate strong priors in form of monocular depth predictors and video-diffusion models. Monocular depth enables us to condition our model on warped reference images for the target views, increasing geometric stability. The video-diffusion prior provides a strong proxy for 3D scenes, allowing the model to learn continuous and pixel-accurate correspondences across generated images. In contrast to approaches relying on autoregressive image generation that are prone to drifts and error accumulation, MultiDiff jointly synthesizes a sequence of frames yielding high-quality and multi-view consistent results -- even for long-term scene generation with large camera movements, while reducing inference time by an order of magnitude. For additional consistency and image quality improvements, we introduce a novel, structured noise distribution. Our experimental results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging, real-world datasets RealEstate10K and ScanNet. Finally, our model naturally supports multi-view consistent editing without the need for further tuning.

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