MooCap：用于牛-物体-人交互与行为动力学的多视角基准
MooCap: A Multi-View Benchmark for Cow-Object-Human Interaction and Behavior Dynamics
该基准提供了多视角同步视频、密集的23种行为标注和39个身体关键点，可用于评估和开发多实体交互感知的时序动作分割与基于关键点轨迹的行为识别模型，尤其是交互密集片段下的性能退化问题值得关注。
原文依据 MooCap contains 42 hours of synchronized multi-camera video from 43 individually tested cows across seven standardized interaction scenarios
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论理解动物行为需要建模身体、物体和其他个体随时间交互的方式，而不仅仅是检测孤立动作或逐帧估计姿态。现有的动物视频数据集主要针对姿态估计或粗粒度的被动观察动作，很少提供研究行为动力学所需的结构化多实体交互标注。我们提出了MooCap，一个在受控实验协议下用于动物-物体-人交互理解的多视角视频基准。MooCap包含来自43头单独测试的牛的42小时同步多摄像机视频，涵盖七种标准化交互场景，包括新环境、新物体、新人、人接近、陌生同类（限制和非限制）以及母牛团聚（限制和非限制）。记录数据密集标注了23种细粒度行为、157个测试会话中的39个身体关键点、4个空间区域和43个受试者，描述了受试者、物体、人和其他牛之间的交互。我们在MooCap上建立了三个基准：（1）对1200-1500秒序列的密集时序动作分割；（2）基于关键点轨迹的姿态行为与交互识别；（3）将成年行为与饲养条件联系起来的纵向行为分类。基准测试结果表明，最优的时序分割模型仅达到66.4%的帧准确率和30.6%的F1@0.5，且在交互密集片段中性能进一步下降。总体而言，MooCap连接了多视角姿态估计、多实体跟踪和结构化行为协议，以支持面向动物行为分析的交互感知模型。
英文原文摘要 1720 字符
Understanding animal behavior requires modeling how bodies, objects, and other agents interact over time, not simply detecting isolated actions or estimating pose frame by frame. Existing animal video datasets target pose estimation or coarse, passively observed actions, and rarely provide the structured, multi-entity interaction annotations needed to study behavioral dynamics. We introduce MooCap, a multi-view video benchmark for animal-object-human interaction understanding under controlled experimental protocols. MooCap contains 42 hours of synchronized multi-camera video from 43 individually tested cows across seven standardized interaction scenarios, including novel environment, novel object, novel human, human approach, unfamiliar conspecifics (restricted and unrestricted) and Dam reunion (restricted and unrestricted). Recordings are densely annotated with 23 fine-grained behaviors, 39 body keypoints across 157 test sessions, 4 spatial zones, and 43 subjects, describing interactions among subjects, objects, humans, and other cattle. We establish three benchmarks on MooCap: (1) dense temporal action segmentation over 1200-1500-second sequences; (2) pose-based behavior and interaction recognition from keypoint trajectories; and (3) longitudinal behavioral classification linking adult behaviors with rearing conditions. Benchmarking results reveal that state-of-the-art temporal segmentation models achieve only 66.4% frame accuracy and 30.6% F1@0.5, with performance degrading further during interaction-heavy segments. Overall, MooCap bridges multi-view pose estimation, multi-entity tracking, and structured behavioral protocols to enable interaction-aware models for animal behavior analysis.