  We present Neural 3D Strokes, a novel technique to generate stylized images
of a 3D scene at arbitrary novel views from multi-view 2D images. Different
from existing methods which apply stylization to trained neural radiance fields
at the voxel level, our approach draws inspiration from image-to-painting
methods, simulating the progressive painting process of human artwork with
vector strokes. We develop a palette of stylized 3D strokes from basic
primitives and splines, and consider the 3D scene stylization task as a
multi-view reconstruction process based on these 3D stroke primitives. Instead
of directly searching for the parameters of these 3D strokes, which would be
too costly, we introduce a differentiable renderer that allows optimizing
stroke parameters using gradient descent, and propose a training scheme to
alleviate the vanishing gradient issue. The extensive evaluation demonstrates
that our approach effectively synthesizes 3D scenes with significant geometric
and aesthetic stylization while maintaining a consistent appearance across
different views. Our method can be further integrated with style loss and
image-text contrastive models to extend its applications, including color
transfer and text-driven 3D scene drawing. Results and code are available at
http://buaavrcg.github.io/Neural3DStrokes.
