PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style
  Mapping
Jiafu Chen Wei Xing Jiakai Sun Tianyi Chu Yiling Huang Boyan Ji Lei Zhao Huaizhong Lin Haibo Chen Zhizhong Wang
3D scene stylization refers to transform the appearance of a 3D scene to
match a given style image, ensuring that images rendered from different
viewpoints exhibit the same style as the given style image, while maintaining
the 3D consistency of the stylized scene. Several existing methods have
obtained impressive results in stylizing 3D scenes. However, the models
proposed by these methods need to be re-trained when applied to a new scene. In
other words, their models are coupled with a specific scene and cannot adapt to
arbitrary other scenes. To address this issue, we propose a novel 3D scene
stylization framework to transfer an arbitrary style to an arbitrary scene,
without any style-related or scene-related re-training. Concretely, we first
map the appearance of the 3D scene into a 2D style pattern space, which
realizes complete disentanglement of the geometry and appearance of the 3D
scene and makes our model be generalized to arbitrary 3D scenes. Then we
stylize the appearance of the 3D scene in the 2D style pattern space via a
prompt-based 2D stylization algorithm. Experimental results demonstrate that
our proposed framework is superior to SOTA methods in both visual quality and
generalization.