Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes
This addresses the problem of creating artistically stylized 3D scenes for applications like digital art and visualization, though it builds incrementally on existing image-to-painting and neural radiance field methods.
The paper tackles 3D scene stylization by generating stylized images from multi-view 2D images using vectorized 3D strokes, achieving consistent appearance across views with significant geometric and aesthetic stylization.
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.