将人体与服装分离重建，实现可动画的多层3D虚拟人像。
LayerGS: Decomposition and Inpainting of Layered 3D Human Avatars via 2D Gaussian Splatting
- 用2D高斯表示每层，精准还原几何与光影。
- 通过扩散模型修复遮挡区域，提升细节完整性。
- 适合虚拟试衣、沉浸式应用等高保真3D人像需求。
我们提出一种新框架，将任意姿态的人体分解为可动画的多层3D虚拟人像，分离身体与衣物。传统单层重建方法使衣物绑定于单一身份，而现有多层方法在遮挡区域表现不佳。我们通过将每层编码为2D高斯点，实现精确几何与照片级渲染，并利用预训练2D扩散模型结合得分蒸馏采样（SDS） inpainting 隐含区域。采用三阶段训练策略：先通过单层重建获得粗粒度的服装基准，再联合优化内层身体与外层衣物细节。在4D-Dress和Thuman2.0两个3D人体基准数据集上的实验表明，本方法在渲染质量、分层分解与重组方面均优于当前最优方法，支持新视角与新姿态下的逼真虚拟试衣，推动高保真3D人像资产在沉浸式应用中的实用化。代码已开源：https://github.com/RockyXu66/LayerGS。
原文摘要 · Abstract (English)
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 https://github.com/RockyXu66/LayerGS
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。