The field of human motion understanding and interaction is moving towards more comprehensive and nuanced approaches, with a focus on integrating language and vision to enable better decision-making and more transparent systems. Researchers are exploring new tasks, such as question answering on time series data and dense motion captioning, to improve the accessibility and usability of human motion models. The development of large-scale datasets and novel models is driving progress in this area, with a particular emphasis on scene comprehension and semantic understanding. Noteworthy papers include:

• QuAnTS, which proposes a novel time series QA dataset and lays the groundwork for deeper exploration of TSQA.
• Dense Motion Captioning, which introduces a new task and dataset for temporally localizing and captioning actions within 3D human motion sequences.
• Human Motion Synthesis in 3D Scenes via Unified Scene Semantic Occupancy, which proposes a framework for human motion synthesis that takes into account scene semantics and structure.
• MCAD, which presents an end-to-end pipeline for generating audio descriptions for soccer games without relying on ground truth AD.