Stylizing Sparse-View 3D Scenes with Hierarchical Neural Representation
<|reference_start|>Stylizing Sparse-View 3D Scenes with Hierarchical Neural Representation: Recently, a surge of 3D style transfer methods has been proposed that leverage the scene reconstruction power of a pre-trained neural radiance field (NeRF). To successfully stylize a scene this way, one must first reconstruct a photo-realistic radiance field from collected images of the scene. However, when only sparse input views are available, pre-trained few-shot NeRFs often suffer from high-frequency artifacts, which are generated as a by-product of high-frequency details for improving reconstruction quality. Is it possible to generate more faithful stylized scenes from sparse inputs by directly optimizing encoding-based scene representation with target style? In this paper, we consider the stylization of sparse-view scenes in terms of disentangling content semantics and style textures. We propose a coarse-to-fine sparse-view scene stylization framework, where a novel hierarchical encoding-based neural representation is designed to generate high-quality stylized scenes directly from implicit scene representations. We also propose a new optimization strategy with content strength annealing to achieve realistic stylization and better content preservation. Extensive experiments demonstrate that our method can achieve high-quality stylization of sparse-view scenes and outperforms fine-tuning-based baselines in terms of stylization quality and efficiency.<|reference_end|>
@article{wang2024stylizing,
    title={Stylizing Sparse-View 3D Scenes with Hierarchical Neural Representation},
    author={Y. Wang, A. Gao, Y. Gong and Y. Zeng},
    journal={arXiv preprint arXiv:2404.05236},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05236},
    primaryClass={cs.CV cs.GR}
}