The field of human motion understanding and generation is moving towards unified frameworks that can handle diverse interaction scenarios and promote knowledge sharing. Researchers are focusing on developing models that can capture fine-grained spatial dependencies, relational reasoning, and compound interaction modeling. Active perception strategies and probabilistic prediction methods are being explored to improve motion forecasting and generation. Noteworthy papers include Uni-Inter, which introduces a unified framework for human motion generation, and Breaking the Passive Learning Trap, which proposes an active perception strategy for human motion prediction. Additionally, UniHOI presents a unified token space for human-object interaction understanding, and MMCM introduces a multimodality-aware metric for probabilistic human motion prediction. These innovative approaches are advancing the field and achieving state-of-the-art performance in various tasks.