We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subjects is a challenging task due to occlusions and complex shapes. Hence, existing multi-view systems typically fuse 3D surfaces of close subjects into a single, connected mesh. To address this issue we leverage i) individually fitted neural implicit avatars; ii) an alternating optimization scheme that refines pose and surface through periods of close proximity; and iii) thus segment the fused raw scans into individual instances. From these instances we compile Hi4D dataset of 4D textured scans of 20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D contains rich interaction-centric annotations in 2D and 3D alongside accurately registered parametric body models. We define varied human pose and shape estimation tasks on this dataset and provide results from state-of-the-art methods on these benchmarks.

翻译：我们提出Hi4D，一种用于长时间接触下物理近距离人际交互自动分析方法与数据集。由于遮挡和复杂形状，稳健分离多个接触对象是一项具有挑战性的任务。因此，现有多视角系统通常将近距离对象的3D表面融合为单个连通网格。为解决此问题，我们利用：i）个体化拟合的神经隐式虚拟化身；ii）一种交替优化方案，在近距离接触期间细化姿态和表面；iii）从而将融合的原始扫描分割为独立实例。基于这些实例，我们构建了Hi4D数据集，包含20组对象对、100个序列及总计超过11K帧的4D纹理扫描。Hi4D提供2D和3D中丰富的以交互为中心的注释，以及精确配准的参数化人体模型。我们在此数据集上定义了多样的人体姿态与形状估计任务，并提供了各领域前沿方法在这些基准上的结果。