MultiDiff: Consistent Novel View Synthesis from a Single Image
<|reference_start|>MultiDiff: Consistent Novel View Synthesis from a Single Image: 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.<|reference_end|>
@article{müller2024multidiff:,
    title={MultiDiff: Consistent Novel View Synthesis from a Single Image},
    author={Norman M"uller, Katja Schwarz, Barbara Roessle, Lorenzo Porzi, Samuel
  Rota Bul`o, Matthias Nie{ss}ner, Peter Kontschieder},
    journal={arXiv preprint arXiv:2406.18524},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18524},
    primaryClass={cs.CV}
}