We present a generalizable feed-forward Gaussian splatting framework for human 3D reconstruction and real-time animation that operates directly on multi-view RGB images and their associated SMPL-X poses. Unlike prior methods that rely on depth supervision, fixed input views, UV map, or repeated feed-forward inference for each target view or pose, our approach predicts, in a canonical pose, a set of 3D Gaussian primitives associated with each SMPL-X vertex. One Gaussian is regularized to remain close to the SMPL-X surface, providing a strong geometric prior and stable correspondence to the parametric body model, while an additional small set of unconstrained Gaussians per vertex allows the representation to capture geometric structures that deviate from the parametric surface, such as clothing and hair. In contrast to recent approaches such as HumanRAM, which require repeated network inference to synthesize novel poses, our method produces an animatable human representation from a single forward pass; by explicitly associating Gaussian primitives with SMPL-X vertices, the reconstructed model can be efficiently animated via linear blend skinning without further network evaluation. We evaluate our method on the THuman 2.1, AvatarReX and THuman 4.0 datasets, where it achieves reconstruction quality comparable to state-of-the-art methods while uniquely supporting real-time animation and interactive applications. Code and pre-trained models are available at https://github.com/Devdoot57/HumanGS .

翻译：我们提出了一种普适的前馈式高斯泼溅框架，用于人体三维重建与实时动画，该框架可直接处理多视角RGB图像及其对应的SMPL-X姿态参数。与依赖深度监督、固定输入视角、UV映射或针对每个目标视角/姿态反复进行前馈推理的现有方法不同，我们的方法在标准姿态下预测一组与每个SMPL-X顶点关联的三维高斯基元。每个高斯基元被约束为紧密贴合SMPL-X表面，从而提供强大的几何先验以及与参数化人体模型的稳定对应关系；同时，每个顶点额外配备少量无约束高斯基元，使表示能够捕捉偏离参数化表面的几何结构（如衣物和头发）。与HumanRAM等近期需重复网络推理以合成新姿态的方法相比，本方法通过单次前向传播即可生成可动画化的人体表示——通过将高斯基元显式关联至SMPL-X顶点，重建模型可经由线性混合蒙皮高效驱动而无需进一步网络计算。我们在THuman 2.1、AvatarReX和THuman 4.0数据集上评估了该方法，其在达到与现有最优方法相当的重建质量的同时，独特地支持实时动画与交互式应用。代码与预训练模型已开源至https://github.com/Devdoot57/HumanGS 。