We present Paint Neural Stroke Field (PaintNeSF), 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.

我们提出了一种新技术 Paint Neural Stroke Field（PaintNeSF），用于根据多视角 2D 图像生成 3D 场景的任意新视角的风格化图像。

PaintNeSF: 用矢量化的 3D 笔画进行风格化景物的艺术创作

PaintNeSF: Artistic Creation of Stylized Scenes with Vectorized 3D  Strokes

This work presents CLIPDraw, an algorithm that synthesizes novel drawings
based on natural language input. CLIPDraw does not require any training; rather
a pre-trained CLIP language-image encoder is used as a metric for maximizing
similarity between the given description and a generated drawing. Crucially,
CLIPDraw operates over vector strokes rather than pixel images, a constraint
that biases drawings towards simpler human-recognizable shapes. Results compare
between CLIPDraw and other synthesis-through-optimization methods, as well as
highlight various interesting behaviors of CLIPDraw, such as satisfying
ambiguous text in multiple ways, reliably producing drawings in diverse
artistic styles, and scaling from simple to complex visual representations as
stroke count is increased. Code for experimenting with the method is available
at:
this https URL

本论文提出 CLIPDraw 算法，它利用自然语言输入来生成新的绘画。CLIPDraw 不需要任何训练，而是使用预训练的 CLIP 语言 - 图像编码器作为优化相似度的指标，通过矢量笔划生成图像，从而呈现出更简洁的形状。该算法与其他优化方法的综合比较以及其可靠的多样化风格的生成能力被强调，并且其代码可供实验使用。