像梵高一样思考：通过流引导的3D高斯点划法实现结构感知的风格迁移
Thinking Like Van Gogh: Structure-Aware Style Transfer via Flow-Guided 3D Gaussian Splatting
AI总结：
本文提出一种流引导的3D高斯点划法框架，通过将二维艺术运动转换为三维高斯几何，实现结构感知的风格迁移，同时采用亮度-结构解耦策略和VLM-as-a-Judge评估框架，提升艺术真实感。
摘要：
1888年，文森特·梵高写道，'我在追求本质中的夸张'。这一原则，即放大结构形式而抑制摄影细节，是后印象派艺术的核心。然而，大多数现有的3D风格迁移方法违背了这一哲学，将几何视为刚性的基底用于表面纹理投影。为了真实再现后印象派的风格化，几何抽象必须作为主要的表达媒介。我们提出了一种流引导的几何运动框架，用于3D高斯点划法（3DGS），在无网格设置中实现了这一原则。我们的方法从二维绘画中提取方向性流场，并将其反向传播到三维空间，将高斯原语校正为流对齐的笔触，使其符合场景拓扑结构，而无需依赖显式的网格先验。这使结构变形能够直接由画家运动驱动，而不是依赖光度约束。我们的贡献有三：（1）一种基于投影的无网格流引导机制，将二维艺术运动转换为三维高斯几何；（2）一种亮度-结构解耦策略，将几何变形与颜色优化分离，减轻在激烈结构抽象中的伪影；（3）一种VLM-as-a-Judge评估框架，通过审美判断而非传统像素级指标评估艺术真实性，明确解决艺术风格化的主观性。
英文摘要：
In 1888, Vincent van Gogh wrote, "I am seeking exaggeration in the essential." This principle, amplifying structural form while suppressing photographic detail, lies at the core of Post-Impressionist art. However, most existing 3D style transfer methods invert this philosophy, treating geometry as a rigid substrate for surface-level texture projection. To authentically reproduce Post-Impressionist stylization, geometric abstraction must be embraced as the primary vehicle of expression. We propose a flow-guided geometric advection framework for 3D Gaussian Splatting (3DGS) that operationalizes this principle in a mesh-free setting. Our method extracts directional flow fields from 2D paintings and back-propagates them into 3D space, rectifying Gaussian primitives to form flow-aligned brushstrokes that conform to scene topology without relying on explicit mesh priors. This enables expressive structural deformation driven directly by painterly motion rather than photometric constraints. Our contributions are threefold: (1) a projection-based, mesh-free flow guidance mechanism that transfers 2D artistic motion into 3D Gaussian geometry; (2) a luminance-structure decoupling strategy that isolates geometric deformation from color optimization, mitigating artifacts during aggressive structural abstraction; and (3) a VLM-as-a-Judge evaluation framework that assesses artistic authenticity through aesthetic judgment instead of conventional pixel-level metrics, explicitly addressing the subjective nature of artistic stylization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
