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.

翻译：我们提出"几何风格化"这一保持身份特征的三维网格风格化新方法。现有技术要么通过凹凸贴图等过度限制的变形方式严格遵循原始形状，要么采用可能引入伪影或改变源形状身份特征的表现性变形来显著修改输入形状。与此不同，我们将三角形网格的变形表示为每个顶点邻域的目标法向量。通过目标法向量重建的变形既具有足够的表现力以实现精细风格化，又具备必要的限制性以保持形状的身份特征。我们采用新颖的可微分"尽可能刚性"层实现此类变形，该层是经典ARAP算法的神经网络适配版本，用于求解逐顶点旋转和变形顶点。作为可微分层，dARAP与文本到图像模型的视觉损失函数相结合，驱动变形朝向风格提示，共同构成了我们的几何风格化方法。项目页面位于https://threedle.github.io/geometry-in-style。