DAMA：解耦的体锚定高斯用于可控的多层avatar
DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars
AI总结：
本文提出DAMA方法，通过专门的表示和重建方法，生成具有物理合理性的穿衣avatar，实现了可控的多层结构、清晰的衣物分离和显式的堆叠控制。
摘要：
现有的3D穿衣avatar重建方法虽然能实现高视觉保真度，但忽略了几何结构和物理合理性。它们要么将穿衣人类建模为单个可变形表面，要么尝试衣物解耦但不强制几何约束，导致衣物边界模糊且无法控制堆叠或层顺序。为解决这些限制，我们引入DAMA（Disentangled body-Anchored Gaussians for Controllable Multi-layered Avatars），一种3D avatar重建方法，通过专门的表示和重建方法生成具有物理合理性的穿衣avatar。在表示层面，我们通过重心平面坐标和正向法线偏移将高斯绑定到SMPL-X面部。基于此参数化，重建方法将2D分割提升为体锚定高斯，利用拓扑引导的修正细化层，并联合优化几何和外观。DAMA是首个从多视角图像生成具有物理合理性的多层avatar的高斯avatar重建方法，实现了清晰的衣物分离和显式的堆叠控制。在完整的4D-DRESS数据集（82扫描）上，DAMA在几何重建、衣物分离、穿透率和穿透深度方面均达到最先进的性能。该表示还支持用户定义的衣物重排和快速将符合身体的衣物转换为模拟准备的网格。项目页面：https://danieleskandar.github.io/dama/
英文摘要：
Existing 3D clothed avatar reconstruction methods achieve high visual fidelity but ignore geometric structure and physical plausibility. They either model clothed humans as a single deformable surface or attempt garment disentanglement without enforcing geometric constraints, resulting in ambiguous garment boundaries and no control over stacking or layer ordering. To address these limitations, we introduce DAMA (Disentangled body-Anchored Gaussians for Controllable Multi-layered Avatars), a 3D avatar reconstruction method that produces physically plausible clothed avatars through a dedicated representation and reconstruction method. At the representation level, we bind Gaussians to SMPL-X faces using barycentric in-plane coordinates and a positive normal offset. Based on this parameterization, the reconstruction method lifts 2D segmentations to body-anchored Gaussians, refines layers using topology-guided correction, and jointly optimizes geometry and appearance. DAMA is the first Gaussian avatar reconstruction method from multi-view images to achieve physically plausible layering, clean garment separation, and explicit stacking control. On the full 4D-DRESS dataset (82 scans), it achieves state-of-the-art performance in geometry reconstruction, garment separation, penetration rate, and penetration depth. The representation further supports user-defined garment reordering and fast conversion of body-conforming garments to simulation-ready meshes. Project Page: https://danieleskandar.github.io/dama/
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。
