用法向量变形实现3D模型风格化，既保形又不失细节。
Geometry in Style: 3D Stylization via Surface Normal Deformation
- 以顶点邻域目标法向量定义网格变形，平衡风格与保形。
- 通过可微ARAP层计算顶点旋转与新位置，支持神经网络优化。
- 适配文生图模型，让文本提示精准驱动3D风格化效果。
我们提出Geometry in Style，一种保持身份的网格风格化方法。现有技术或因使用凹凸贴图等限制性变形而保留原形，或因表达性强的变形导致失真或改变形状身份。我们创新地将三角网格变形表示为每个顶点邻域的目标法向量，恢复的变形既具表现力以实现精细风格化，又足够受限以保留形状身份。通过新型可微ARAP（dARAP）层——经典ARAP算法的神经网络兼容版本——求解每顶点的旋转和变形后位置。该可微层结合文生图模型的视觉损失，引导变形符合风格提示，从而实现几何与风格的统一。项目主页见https://threedle.github.io/geometry-in-style。
原文摘要 · Abstract (English)
We present Geometry in Style, a new method for identity-preserving mesh stylization. Existing techniques either adhere to the original shape through overly restrictive deformations such as bump maps or significantly modify the input shape using expressive deformations that may introduce artifacts or alter the identity of the source shape. In contrast, we represent a deformation of a triangle mesh as a target normal vector for each vertex neighborhood. The deformations we recover from target normals are expressive enough to enable detailed stylizations yet restrictive enough to preserve the shape's identity. We achieve such deformations using our novel differentiable As-Rigid-As-Possible (dARAP) layer, a neural-network-ready adaptation of the classical ARAP algorithm which we use to solve for per-vertex rotations and deformed vertices. As a differentiable layer, dARAP is paired with a visual loss from a text-to-image model to drive deformations toward style prompts, altogether giving us Geometry in Style. Our project page is at https://threedle.github.io/geometry-in-style.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。