As a fundamental aspect of human life, two-person interactions contain
meaningful information about people's activities, relationships, and social
settings. Human action recognition serves as the foundation for many smart
applications, with a strong focus on personal privacy. However, recognizing
two-person interactions poses more challenges due to increased body occlusion
and overlap compared to single-person actions. In this paper, we propose a
point cloud-based network named Two-stream Multi-level Dynamic Point
Transformer for two-person interaction recognition. Our model addresses the
challenge of recognizing two-person interactions by incorporating local-region
spatial information, appearance information, and motion information. To achieve
this, we introduce a designed frame selection method named Interval Frame
Sampling (IFS), which efficiently samples frames from videos, capturing more
discriminative information in a relatively short processing time. Subsequently,
a frame features learning module and a two-stream multi-level feature
aggregation module extract global and partial features from the sampled frames,
effectively representing the local-region spatial information, appearance
information, and motion information related to the interactions. Finally, we
apply a transformer to perform self-attention on the learned features for the
final classification. Extensive experiments are conducted on two large-scale
datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The
results show that our network outperforms state-of-the-art approaches across
all standard evaluation settings.

通过融合局部区域空间信息、外观信息和动作信息，本文提出了一种基于点云的两人交互识别网络。采用一种名为时间间隔帧采样（IFS）的设计帧选择方法，从视频中高效采样帧，捕捉到相对较短的处理时间内更具区分性的信息。然后，通过一个帧特征学习模块和一个两流多级特征聚合模块，从采样帧中提取全局和局部特征，有效地表示与交互相关的局部区域空间信息、外观信息和动作信息。最后，我们应用一种 transformer 对学习到的特征实施自注意力机制进行最终分类。在两个大规模数据集 NTU RGB+D 60 和 NTU RGB+D 120 上进行了大量实验，结果表明我们的网络在所有标准评估设置下优于现有的方法。

双流多级动态点变换器用于两人互动识别

Two-stream Multi-level Dynamic Point Transformer for Two-person  Interaction Recognition

In this paper, we propose a novel method for representation and
classification of two-person interactions from 3D skeleton sequences. The key
idea of our approach is to use Gaussian distributions to capture statistics on
R n and those on the space of symmetric positive definite (SPD) matrices. The
main challenge is how to parametrize those distributions. Towards this end, we
develop methods for embedding Gaussian distributions in matrix groups based on
the theory of Lie groups and Riemannian symmetric spaces. Our method relies on
the Riemannian geometry of the underlying manifolds and has the advantage of
encoding high-order statistics from 3D joint positions. We show that the
proposed method achieves competitive results in two-person interaction
recognition on three benchmarks for 3D human activity understanding.

提出了一种基于高斯分布和李群、黎曼对称空间的新方法，可用于表示和分类二人互动的三维骨架序列，并在三个三维人体活动理解基准测试中取得了有竞争力的结果。