Multi-person pose forecasting has garnered increasing attention owing to its critical role in applications such as human-robotic interaction and autonomous systems. Existing methods have achieved notable progress by integrating interaction-aware mechanisms to capture relational dynamics among individuals. However, these approaches often suffer from two key limitations: (1) the implicit assumption that all individuals within a scene are socially relevant, and (2) the uniform treatment of all body joints, regardless of their activity levels or importance in motion representation. To address these challenges, we propose a novel Transformer-based architecture, termed AsymFormer, which is designed to asymmetrically model both human-level interactions and joint-level dynamics. At the human level, we introduce a Selective Interaction-Aware Module (SIAM), which leverages a discriminator pre-trained on ground-truth interaction labels to evaluate the social relevance of each individual based on their historical motion trajectories. This design enables the model to explicitly focus on meaningful interactions while filtering out socially irrelevant agents. At the joint level, we develop a Targeted Joints Dynamic Encoder (TJDE) to emphasize high-activity joints, thereby avoiding the dilution of informative motion signals caused by the equal treatment of all joints. This selective focus enhances the discriminative power of motion representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches in multi-person 3D pose forecasting.

Unsupervised action recognition based on spatiotemporal fusion feature extraction has attracted much attention in recent years. However, existing methods still have several limitations: (1) The long-term dependence relationship is not effectively extracted at the time level. (2) The high-order motion relationship between non-adjacent nodes is not effectively captured at the spatial level. (3) The model complexity is too high when the cascade layer input sequence is long, or there are many key points. To solve these problems, a Multiple Distilling-based spatial-temporal attention (MD-STA) networks is proposed in this paper. This model can extract temporal and spatial features respectively and fuse them. Specifically, we first propose a Screening Self-attention (SSA) module; this module can find long-term dependencies in distant frames and high-order motion patterns between non-adjacent nodes in a single frame through a sparse metric on dot product pairs. Then, we propose the Frames and Keypoint-Distilling (FKD) module, which uses extraction operations to halve the input of the cascade layer to eliminate invalid key points and time frame features, thus reducing time and memory complexity. Finally, the Dim-reduction Fusion (DRF) module is proposed to reduce the dimension of existing features to further eliminate redundancy. Numerous experiments were conducted on three distinct datasets: NTU-60, NTU-120, and UWA3D, showing that MD-STA achieves state-of-the-art standards in skeleton-based unsupervised action recognition.

Graph convolutional network-based methods have recently shown promising performance in skeleton-based data processing. However, these methods have two critical issues in skeleton-based motion prediction tasks: First, graph modeling of motion poses is based on the fixed graph according to the physical connection of human joints and ignores the exploration of deep implicit information based on human dynamic kinetics. Second, existing methods usually use motion information in a single semantic space to model the whole motion sequences, underestimating diverse semantic patterns for improving the modeling ability. To address the first issue, we propose the Attention-based Dynamic Graph Convolution method, which tries to capture implicit semantic information dynamically. To address the second issue, we propose the Kinematic-based Semantics Aggregation Block (KSAB), which combines various semantic features from four semantic perspectives to rich motion representation. Integrating the above two designs, we propose a novel Multi-Semantics Aggregation Network (MANet), resulting in more comprehensive feature extraction in dynamic implicit semantics learning to enhance motion prediction. Extensive experiments are conducted to validate the effectiveness of MANet, which outperforms state-of-the-art methods by 10.9%, 6.6%, and 19.6% in terms of MPJPE for motion prediction on Human3.6M, CMU Mocap, and 3DPW datasets, respectively.