从单图重建可动画的衣物感知3D人像，支持衣物独立编辑与试穿。
Forwardrobe: Garment-Aware Gaussian Avatars from a Single Image
- 将衣物与身体在初始空间中分离，用连续性感知初始化衣物几何与绑定。
- 通过姿态条件非刚性变形和外观自适应，显著提升裙装等松散衣物动画质量。
- 生成可独立控制的3D衣物资产，适合虚拟试衣、换装与创意设计。
从单张图像重建可动画的3D人体角色仍面临巨大挑战，尤其对于松散衣物，其几何与运动难以通过身体对齐的拓扑结构和绑定方式准确表示。本文提出Forwardrobe，一种前馈式框架，可从单图重建衣物感知的高斯人体模型。Forwardrobe在标准高斯空间中显式分离衣物与身体，并为衣物层配备连续性感知的几何与绑定初始化、姿态条件的非刚性形变以及外观自适应机制。这些设计有效提升了衣物重建质量与动画过程中的视觉表现，尤其适用于长裙与连衣裙。分离后的衣物层还可作为独立可控的3D资产，支持衣物编辑、迁移与3D虚拟试穿。实验表明，相比现有单图人体重建方法，Forwardrobe在衣物重建质量与衣物操控灵活性方面均有显著提升。
原文摘要 · Abstract (English)
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.
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