Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes
Hao-Bin Duan, Miao Wang, Yan-Xun Li, Yong-Liang Yang

TL;DR
Neural 3D Strokes introduces a novel method for stylizing 3D scenes using vectorized strokes optimized via differentiable rendering, enabling consistent multi-view stylization and extensions like color transfer and text-driven drawing.
Contribution
It presents a new approach that simulates human painting with vector strokes for 3D scene stylization, optimizing stroke parameters through a differentiable renderer and gradient descent.
Findings
Effective synthesis of stylized 3D scenes with consistent multi-view appearance
Ability to extend to color transfer and text-driven scene drawing
Demonstrates significant geometric and aesthetic stylization
Abstract
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…
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Taxonomy
TopicsArt, Technology, and Culture · Artistic and Creative Research · Aesthetic Perception and Analysis
