AnySurf: 基于有向边的任意表面生成
AnySurf: Any Surface Generation with Directed Edge
AI总结：
提出AnySurf统一框架，通过有向边增强的柔性双网格表示，实现开放、封闭和混合3D表面的高质量生成，并引入ROS-FT后训练和轻量级DE-Adapter以保持生成性能。
摘要：
开放表面组件在真实工业3D内容中普遍存在，支持渲染、物理模拟和几何编辑。服装作为典型的开放表面类型，现有许多生成方法利用缝纫图案生成2D面板并缝合为3D形状。这种特定领域的设计缺乏可扩展性，无法泛化到鞋子和配饰。常见的基于场的3D生成器优先考虑水密网格，并倾向于在开放表面上创建有缺陷的双层结构。尽管Trellis2采用了无场表示，但其开放表面结果仍存在法线和拓扑错误。我们提出AnySurf，一个统一框架，生成具有准确面朝向的开放、封闭和混合3D表面。基于有向边增强的柔性双网格（FDG-D），我们的表示通过定向网格边保留法线方向信息。我们还提出了ROS-FT后训练和仅增加1%额外参数的轻量级DE-Adapter，促进有向边学习同时保持原始生成性能。我们进一步构建了包含工业服装和封闭配件的Outfit3D数据集。我们的工作将服装建模转化为通用的3D生成任务。实验结果表明，在网格质量和下游应用实用性方面具有优越性。
英文摘要：
Open surface components prevail in real industrial 3D content and support rendering, physical simulation and geometric editing. Garments serve as a typical open surface type, with numerous existing generation methods leveraging sewing patterns to generate 2D panels and stitch them into 3D shapes. Such domain-specific designs lack scalability and cannot generalize to shoes and accessories. Common field-based 3D generators prioritize watertight meshes and tend to create flawed double-layer structures on open surfaces. Though Trellis2 adopts field-free representation, its open surface results still contain normal and topology errors. We present AnySurf, a unified framework generating open, closed and hybrid 3D surfaces with accurate face orientation. Built on directed-edge enhanced Flexible Dual Grid (FDG-D), our representation retains normal direction information via oriented grid edges. We also propose ROS-FT post-training and a lightweight DE-Adapter with merely 1% extra parameters, facilitating directed edge learning while preserving original generation performance. We further construct Outfit3D dataset containing industrial garments and closed accessories. Our work transforms garment modeling into a universal 3D generation task. Experimental results demonstrate superior mesh quality and better practicality for downstream applications.
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。
