Reconstructing 3D human bodies from realistic motion sequences remains a challenge due to pervasive and complex occlusions. Current methods struggle to capture the dynamics of occluded body parts, leading to model penetration and distorted motion. RemoCap leverages Spatial Disentanglement (SD) and Motion Disentanglement (MD) to overcome these limitations. SD addresses occlusion interference between the target human body and surrounding objects. It achieves this by disentangling target features along the dimension axis. By aligning features based on their spatial positions in each dimension, SD isolates the target object's response within a global window, enabling accurate capture despite occlusions. The MD module employs a channel-wise temporal shuffling strategy to simulate diverse scene dynamics. This process effectively disentangles motion features, allowing RemoCap to reconstruct occluded parts with greater fidelity. Furthermore, this paper introduces a sequence velocity loss that promotes temporal coherence. This loss constrains inter-frame velocity errors, ensuring the predicted motion exhibits realistic consistency. Extensive comparisons with state-of-the-art (SOTA) methods on benchmark datasets demonstrate RemoCap's superior performance in 3D human body reconstruction. On the 3DPW dataset, RemoCap surpasses all competitors, achieving the best results in MPVPE (81.9), MPJPE (72.7), and PA-MPJPE (44.1) metrics. Codes are available at https://wanghongsheng01.github.io/RemoCap/.

翻译：从真实运动序列中重建三维人体仍因普遍且复杂的遮挡而面临挑战。现有方法难以捕捉被遮挡身体部位的动态变化，导致模型穿透和运动失真。RemoCap利用空间解耦（SD）与运动解耦（MD）克服了这些局限。SD通过沿维度轴解耦目标特征，解决了目标人体与周围物体之间的遮挡干扰。通过根据各维度空间位置对齐特征，SD在全局窗口内隔离目标物体的响应，从而在遮挡情况下实现精准捕获。MD模块采用通道维度的时序混洗策略模拟多样化场景动态，该过程有效解耦运动特征，使RemoCap能够以更高保真度重建被遮挡部位。此外，本文引入序列速度损失以增强时间连贯性，该损失通过约束帧间速度误差，确保预测运动呈现真实一致性。在基准数据集上与最新方法（SOTA）的广泛对比表明，RemoCap在三维人体重建中表现卓越。在3DPW数据集上，RemoCap超越所有竞争对手，在MPVPE（81.9）、MPJPE（72.7）和PA-MPJPE（44.1）指标上均取得最优结果。代码开源地址：https://wanghongsheng01.github.io/RemoCap/。