Abstract
We propose a novel framework for decomposing arbitrarily posed humans into animatable multi-layered 3D human avatars, separating the body and garments. Conventional single-layer reconstruction methods lock clothing to one identity, while prior multi-layer approaches struggle with occluded regions. We overcome both limitations by encoding each layer as a set of 2D Gaussians for accurate geometry and photorealistic rendering, and inpainting hidden regions with a pretrained 2D diffusion model via score-distillation sampling (SDS). Our three-stage training strategy first reconstructs the coarse canonical garment via single-layer reconstruction, followed by multi-layer training to jointly recover the inner-layer body and outer-layer garment details. Experiments on two 3D human benchmark datasets (4D-Dress, Thuman2.0) show that our approach achieves better rendering quality and layer decomposition and recomposition than the previous state-of-the-art, enabling realistic virtual try-on under novel viewpoints and poses, and advancing practical creation of high-fidelity 3D human assets for immersive applications. Our code is available at this https URL
Abstract (translated)
我们提出了一种新的框架，用于将任意姿态的人体分解为可动画的多层3D人体化身，将身体和服装分开。传统的单层重建方法会将服装固定在一个身份上，而先前的多层方法则难以处理被遮挡区域的问题。通过编码每一层为一组2D高斯分布，并使用预训练的二维扩散模型通过评分蒸馏采样（SDS）来填补隐藏区域，我们克服了这些限制以实现准确的几何结构和逼真的渲染效果。我们的三阶段训练策略首先通过单层重建粗略地重构服装，然后进行多层训练以同时恢复内层身体和外层服装细节。 在两个3D人体基准数据集（4D-Dress 和 Thuman2.0）上的实验表明，与之前的最新技术相比，我们的方法实现了更好的渲染质量和层次分解重组效果。这使得在新的视角和姿势下进行逼真的虚拟试穿成为可能，并推动了高保真度的3D人类资产在沉浸式应用中的实际创建。我们的代码可在 [这个链接](https://this-URL) 获取。
URL
https://arxiv.org/abs/2601.05853
