Stylizing Sparse-View 3D Scenes with Hierarchical Neural Representation
Y. Wang A. Gao Y. Gong Y. Zeng
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.