Aligning a template to 3D human point clouds is a long-standing problem crucial for tasks like animation, reconstruction, and enabling supervised learning pipelines. Recent data-driven methods leverage predicted surface correspondences; however, they are not robust to varied poses or distributions. In contrast, industrial solutions often rely on expensive manual annotations or multi-view capturing systems. Recently, neural fields have shown promising results, but their purely data-driven nature lacks geometric awareness, often resulting in a trivial misalignment of the template registration. In this work, we propose two solutions: LoVD, a novel neural field model that predicts the direction towards the localized SMPL vertices on the target surface; and INT, the first self-supervised task dedicated to neural fields that, at test time, refines the backbone, exploiting the target geometry. We combine them into INLoVD, a robust 3D Human body registration pipeline trained on a large MoCap dataset. INLoVD is efficient (takes less than a minute), solidly achieves the state of the art over public benchmarks, and provides unprecedented generalization on out-of-distribution data. We will release code and checkpoints in \url{url}.

论文提出了两种解决方案：一种是通过预测目标表面上局部SMPL顶点的方向来预测模板与3D人体点云的对齐问题的新型神经场模型LoVD；另一种是在测试时利用目标几何信息对骨干进行改进的首个自监督任务INT。将它们结合成INLoVD，这是一个在大规模MoCap数据集上训练的稳健的3D人体注册流水线，高效（不到一分钟）、在公共基准测试中稳定达到最先进水平，并在分布外数据上提供了前所未有的泛化能力。

基于神经网络场的几何意识在3D人体配准中的应用