ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
<|reference_start|>ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis: Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose \textbf{ViewCrafter}, a novel method for synthesizing high-fidelity novel views of generic scenes from single or sparse images with the prior of video diffusion model. Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with precise camera pose control. To further enlarge the generation range of novel views, we tailored an iterative view synthesis strategy together with a camera trajectory planning algorithm to progressively extend the 3D clues and the areas covered by the novel views. With ViewCrafter, we can facilitate various applications, such as immersive experiences with real-time rendering by efficiently optimizing a 3D-GS representation using the reconstructed 3D points and the generated novel views, and scene-level text-to-3D generation for more imaginative content creation. Extensive experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method in synthesizing high-fidelity and consistent novel views.<|reference_end|>
@article{yu2024viewcrafter:,
    title={ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View
  Synthesis},
    author={Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang,
  Xiangjun Gao, Tien-Tsin Wong, Ying Shan, Yonghong Tian},
    journal={arXiv preprint arXiv:2409.02048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02048},
    primaryClass={cs.CV}
}