用图像风格改造3D网格，实现夸张几何变形而不破坏结构
Image-Guided Geometric Stylization of 3D Meshes
- 通过扩散模型提取图像抽象特征，指导网格几何变形
- 粗到精流程实现多样变形，保持原网格拓扑与部件语义
- 适合艺术创作与个性化3D建模，支持独特轮廓与姿态
近期生成模型可创建视觉可信的3D物体表示，但生成过程常依赖上下文描述等隐式控制信号，难以实现超出数据分布的显著几何扭曲。本文提出一种几何风格化框架，通过变形3D网格使其表达图像的风格特征。尽管风格本身具有模糊性，我们利用预训练扩散模型提取输入图像的抽象表征。提出的粗到精风格化流程可大幅扭曲输入3D模型，呈现丰富的几何变化，同时保持原始网格的有效拓扑和部件级语义。此外，我们设计了一种近似VAE编码器，从网格渲染中提供高效可靠的梯度。大量实验表明，该方法能生成反映图片资产独特几何特征（如表现性姿态与轮廓）的风格化3D网格，从而支持独特艺术化3D创作。项目页面：https://changwoonchoi.github.io/GeoStyle
原文摘要 · Abstract (English)
Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions beyond existing data distributions. We propose a geometric stylization framework that deforms a 3D mesh, allowing it to express the style of an image. While style is inherently ambiguous, we utilize pre-trained diffusion models to extract an abstract representation of the provided image. Our coarse-to-fine stylization pipeline can drastically deform the input 3D model to express a diverse range of geometric variations while retaining the valid topology of the original mesh and part-level semantics. We also propose an approximate VAE encoder that provides efficient and reliable gradients from mesh renderings. Extensive experiments demonstrate that our method can create stylized 3D meshes that reflect unique geometric features of the pictured assets, such as expressive poses and silhouettes, thereby supporting the creation of distinctive artistic 3D creations. Project page: https://changwoonchoi.github.io/GeoStyle
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