With the explosive growth of available training data, single-image 3D human modeling is ahead of a transition to a data-centric paradigm. A key to successfully exploiting data scale is to design flexible models that can be supervised from various heterogeneous data sources produced by different researchers or vendors. To this end, we propose a simple yet powerful paradigm for seamlessly unifying different human pose and shape-related tasks and datasets. Our formulation is centered on the ability - both at training and test time - to query any arbitrary point of the human volume, and obtain its estimated location in 3D. We achieve this by learning a continuous neural field of body point localizer functions, each of which is a differently parameterized 3D heatmap-based convolutional point localizer (detector). For generating parametric output, we propose an efficient post-processing step for fitting SMPL-family body models to nonparametric joint and vertex predictions. With this approach, we can naturally exploit differently annotated data sources including mesh, 2D/3D skeleton and dense pose, without having to convert between them, and thereby train large-scale 3D human mesh and skeleton estimation models that outperform the state-of-the-art on several public benchmarks including 3DPW, EMDB and SSP-3D by a considerable margin.

翻译：随着可用训练数据的爆炸式增长，单图像三维人体建模正迈向以数据为中心的范式转型。成功利用数据规模的关键在于设计灵活的模型，使其能够从不同研究者或供应商提供的多种异构数据源中获得监督。为此，我们提出了一种简洁而强大的范式，用于无缝统一不同人体姿态与形状相关任务及数据集。我们的方法核心在于能够在训练和测试阶段查询人体体积中的任意点，并获取其在三维空间中的估计位置。我们通过学习一个连续神经场来实现这一目标，该场由多个身体点定位器函数构成，每个函数均为基于三维热力图的不同参数化卷积点定位器（检测器）。为生成参数化输出，我们提出了一种高效的后处理步骤，将SMPL系列人体模型拟合至非参数化的关节点与顶点预测结果。通过这种方法，我们能够自然地利用包括网格、二维/三维骨架和密集姿态在内的不同标注数据源，无需进行数据格式转换，从而训练出的大规模三维人体网格与骨架估计模型在3DPW、EMDB和SSP-3D等多个公开基准测试中显著超越现有最优方法。