In this paper, we realize automatic visual recognition and direction estimation of pointing. We introduce the first neural pointing understanding method based on two key contributions. The first is the introduction of a first-of-its-kind large-scale dataset for pointing recognition and direction estimation, which we refer to as the DP Dataset. DP Dataset consists of more than 2 million frames of 33 people pointing in various styles annotated for each frame with pointing timings and 3D directions. The second is DeePoint, a novel deep network model for joint recognition and 3D direction estimation of pointing. DeePoint is a Transformer-based network which fully leverages the spatio-temporal coordination of the body parts, not just the hands. Through extensive experiments, we demonstrate the accuracy and efficiency of DeePoint. We believe DP Dataset and DeePoint will serve as a sound foundation for visual human intention understanding.

翻译：本文实现了对指向动作的自动视觉识别与方向估计。我们提出了首个基于神经网络的指向理解方法，其核心贡献包含两点。其一，我们首次引入大规模指向识别与方向估计数据集（简称DP数据集）。该数据集包含33位测试者以多种风格进行指向的超过200万帧画面，每帧均标注了指向时间点与三维方向。其二，我们提出创新深度网络模型DeePoint，实现指向动作的联合识别与三维方向估计。DeePoint是基于Transformer架构的网络，不仅关注手部动作，更充分利用身体各部位的时空协调性。通过大量实验，我们验证了DeePoint的准确性与高效性。我们相信DP数据集与DeePoint将为视觉人类意图理解奠定坚实基础。