  This paper presents a stylized novel view synthesis method. Applying
state-of-the-art stylization methods to novel views frame by frame often causes
jittering artifacts due to the lack of cross-view consistency. Therefore, this
paper investigates 3D scene stylization that provides a strong inductive bias
for consistent novel view synthesis. Specifically, we adopt the emerging neural
radiance fields (NeRF) as our choice of 3D scene representation for their
capability to render high-quality novel views for a variety of scenes. However,
as rendering a novel view from a NeRF requires a large number of samples,
training a stylized NeRF requires a large amount of GPU memory that goes beyond
an off-the-shelf GPU capacity. We introduce a new training method to address
this problem by alternating the NeRF and stylization optimization steps. Such a
method enables us to make full use of our hardware memory capacity to both
generate images at higher resolution and adopt more expressive image style
transfer methods. Our experiments show that our method produces stylized NeRFs
for a wide range of content, including indoor, outdoor and dynamic scenes, and
synthesizes high-quality novel views with cross-view consistency.
