Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable challenges in achieving realistic clothing deformation. Current methodologies often overlook the influence of motion on surface deformation, resulting in surfaces lacking the constraints imposed by global motion. To overcome these limitations, we introduce an innovative framework, Motion-Based 3D Clothed Humans Synthesis (MOSS), which employs kinematic information to achieve motion-aware Gaussian split on the human surface. Our framework consists of two modules: Kinematic Gaussian Locating Splatting (KGAS) and Surface Deformation Detector (UID). KGAS incorporates matrix-Fisher distribution to propagate global motion across the body surface. The density and rotation factors of this distribution explicitly control the Gaussians, thereby enhancing the realism of the reconstructed surface. Additionally, to address local occlusions in single-view, based on KGAS, UID identifies significant surfaces, and geometric reconstruction is performed to compensate for these deformations. Experimental results demonstrate that MOSS achieves state-of-the-art visual quality in 3D clothed human synthesis from monocular videos. Notably, we improve the Human NeRF and the Gaussian Splatting by 33.94% and 16.75% in LPIPS* respectively. Codes are available at https://wanghongsheng01.github.io/MOSS/.

翻译：摘要：单视角穿衣人体重建在虚拟现实应用中占据核心地位，尤其是在涉及复杂人体运动的场景中。实现逼真的衣物形变仍面临显著挑战。现有方法常忽视运动对表面形变的影响，导致重建表面缺乏全局运动约束。为克服这些局限，我们提出创新框架——基于运动的三维穿衣人体合成（MOSS），该框架利用运动学信息实现人体表面的运动感知高斯分裂。框架包含两个模块：运动学高斯定位溅射（KGAS）与表面形变检测器（UID）。KGAS通过矩阵Fisher分布将全局运动传播至全身表面，该分布中的密度和旋转因子显式控制高斯参数，从而增强重建表面的逼真度。此外，针对单视角下的局部遮挡问题，UID基于KGAS识别关键表面区域，并通过几何重建补偿形变。实验结果表明，MOSS在单目视频三维穿衣人体合成任务中达到了最先进的视觉质量。值得注意的是，我们在LPIPS*指标上分别较Human NeRF与高斯溅射提升了33.94%和16.75%。代码开源于https://wanghongsheng01.github.io/MOSS/。