  In this work, we aim to address the 3D scene stylization problem - generating
stylized images of the scene at arbitrary novel view angles. A straightforward
solution is to combine existing novel view synthesis and image/video style
transfer approaches, which often leads to blurry results or inconsistent
appearance. Inspired by the high-quality results of the neural radiance fields
(NeRF) method, we propose a joint framework to directly render novel views with
the desired style. Our framework consists of two components: an implicit
representation of the 3D scene with the neural radiance fields model, and a
hypernetwork to transfer the style information into the scene representation.
In particular, our implicit representation model disentangles the scene into
the geometry and appearance branches, and the hypernetwork learns to predict
the parameters of the appearance branch from the reference style image. To
alleviate the training difficulties and memory burden, we propose a two-stage
training procedure and a patch sub-sampling approach to optimize the style and
content losses with the neural radiance fields model. After optimization, our
model is able to render consistent novel views at arbitrary view angles with
arbitrary style. Both quantitative evaluation and human subject study have
demonstrated that the proposed method generates faithful stylization results
with consistent appearance across different views.
