Forwardrobe：基于单张图像的衣物感知高斯化身
Forwardrobe: Garment-Aware Gaussian Avatars from a Single Image
- Nanyang Technological University（南洋理工大学）
- The University of Queensland（昆士兰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出Forwardrobe框架，通过在高斯空间分离衣物与身体，实现单张图像的衣物感知高斯化身重建，提升了衣物重建质量与操作灵活性，支持衣物编辑等应用。
AI中文摘要：
从单张图像重建可动画的三维人类化身，对于宽松衣物而言仍极具挑战性，其几何结构与运动无法通过身体对齐拓扑和蒙皮充分表征。我们提出Forwardrobe，一种用于从单张图像重建衣物感知高斯化身的前馈框架。Forwardrobe在规范高斯空间中明确分离衣物与身体，并为衣物层配备感知连续性的几何与蒙皮初始化、姿态条件非刚性变形及外观适配。这些设计提升了动画过程中的衣物重建与视觉质量，尤其针对裙子和连衣裙。分离后的衣物层还形成可独立控制的三维资产，支持衣物编辑、迁移及三维虚拟试穿。实验表明，与现有单张图像化身重建方法相比，该方法在衣物重建质量和衣物操作灵活性上均有提升。
英文摘要：
Reconstructing animatable 3D human avatars from a single image remains particularly challenging for loose garments, whose geometry and motion cannot be adequately represented by body-aligned topology and skinning. We present Forwardrobe, a feed-forward framework for reconstructing garment-aware Gaussian avatars from a single image. Forwardrobe explicitly separates clothing from the body in canonical Gaussian space and equips the garment layer with continuity-aware geometry and skinning initialization, pose-conditioned non-rigid deformation, and appearance adaptation. These designs improve garment reconstruction and visual quality during animation, particularly for skirts and dresses. The separated garment layer additionally forms an independently controllable 3D asset, enabling garment editing, transfer, and 3D virtual try-on. Experiments demonstrate improved garment reconstruction quality and greater flexibility in garment manipulation compared with existing single-image avatar reconstruction methods.
