面向高效高保真绘画创作的双参数化可微笔画规划
Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation
提出离散折线与连续贝塞尔控制点的双向映射，使可微优化能同时利用局部梯度与结构感知的笔画提议；其受高斯泼溅启发的初始化策略支持高度并行的笔画优化，可迁移到其他基于笔画的矢量生成任务（如草图生成、字体设计）。
原文依据 we propose a dual representation that couples discrete polylines with continuous Bezier control points via a bidirectional mapping mechanism.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在基于笔画的渲染中，搜索方法常因离散的笔画放置而陷入局部最优，而可微优化器缺乏结构感知能力，产生无结构的布局。为弥合这一差距，我们提出一种双重表示，通过双向映射机制将离散折线与连续贝塞尔控制点耦合起来。这实现了协同优化：局部梯度精炼全局笔画结构，而内容感知的笔画提议有助于逃离糟糕的局部最优。我们的表示进一步支持受高斯泼溅启发的初始化，从而能够在整个图像上高度并行地优化笔画。实验表明，与现有的可微矢量化方法相比，我们的方法将笔画数量减少30-50%，实现结构更连贯的布局，并提升重建质量，同时将优化时间缩短30-40%。
英文原文摘要 923 字符
In stroke-based rendering, search methods often get trapped in local minima due to discrete stroke placement, while differentiable optimizers lack structural awareness and produce unstructured layouts. To bridge this gap, we propose a dual representation that couples discrete polylines with continuous Bezier control points via a bidirectional mapping mechanism. This enables collaborative optimization: local gradients refine global stroke structures, while content-aware stroke proposals help escape poor local optima. Our representation further supports Gaussian-splatting-inspired initialization, enabling highly parallel stroke optimization across the image. Experiments show that our approach reduces the number of strokes by 30-50%, achieves more structurally coherent layouts, and improves reconstruction quality, while cutting optimization time by 30-40% compared to existing differentiable vectorization methods.