Real-time, high-fidelity 3D human reconstruction from RGB images is essential for interactive applications such as virtual reality and gaming, yet remains challenging due to the complex non-rigid deformations of dynamic human bodies. Although 3D Gaussian Splatting enables efficient rendering, existing methods struggle to capture fine geometric details and often produce artifacts such as fused fingers and over-smoothed faces. Moreover, conventional spatial-field-based dynamic modeling faces a trade-off between reconstruction fidelity and GPU memory consumption. To address these issues, we propose a novel 3D Gaussian human reconstruction framework that combines region-aware initialization with rich geometric priors. Specifically, we leverage the expressive SMPL-X model to initialize both 3D Gaussians and skinning weights, providing a robust geometric foundation for precise reconstruction. We further introduce a region-aware density initialization strategy and a geometry-aware multi-scale hash encoding module to improve local detail recovery while maintaining computational efficiency.Experiments on PeopleSnapshot and GalaBasketball show that our method achieves superior reconstruction quality and finer detail preservation under complex motions, while maintaining real-time rendering speed.

翻译：从RGB图像进行实时、高保真的三维人体重建是虚拟现实和游戏等交互式应用的关键需求，然而由于动态人体复杂的非刚体形变，这一任务仍充满挑战。尽管三维高斯泼溅技术能够实现高效渲染，但现有方法难以捕捉精细的几何细节，且常产生手指粘连、面部过度平滑等伪影。此外，传统的空间场动态建模方法在重建保真度与GPU内存消耗之间存在权衡。为解决这些问题，我们提出了一种新型三维高斯人体重建框架，融合了区域感知初始化与丰富的几何先验。具体而言，我们利用具有高表达力的SMPL-X模型初始化三维高斯与蒙皮权重，为精确重建提供稳健的几何基础。进一步引入区域感知密度初始化策略与几何感知多尺度哈希编码模块，在保持计算效率的同时提升局部细节恢复能力。在PeopleSnapshot与GalaBasketball数据集上的实验表明，本方法在复杂运动场景下能实现更优的重建质量与更精细的细节保持，同时维持实时渲染速度。