Unified Implicit Neural Stylization.

Representing visual signals by implicit neural representation (INR) has prevailed among many vision tasks. Its potential for editing/processing given signals remains less explored. This work explores a new intriguing direction: training a stylized implicit representation, using a generalized approach that can apply to various 2D and 3D scenarios. We conduct a pilot study on a variety of INRs, including 2D coordinate-based representation, signed distance function, and neural radiance field. Our solution is a Unified Implicit Neural Stylization framework, dubbed INS. In contrary to vanilla INR, INS decouples the ordinary implicit function into a style implicit module and a content implicit module, in order to separately encode the representations from the style image and input scenes. An amalgamation module is then applied to aggregate these information and synthesize the stylized output. To regularize the geometry in 3D scenes, we propose a novel self-distillation geometry consistency loss which preserves the geometry fidelity of the stylized scenes. Comprehensive experiments are conducted on multiple task settings, including fitting images using MLPs, stylization for implicit surfaces and sylized novel view synthesis using neural radiance. We further demonstrate that the learned representation is continuous not only spatially but also style-wise, leading to effortlessly interpolating between different styles and generating images with new mixed styles. Please refer to the video on our project page for more view synthesis results: https://zhiwenfan.github.io/INS.