首个动态目标抓取基准，支持1000万帧真实交互数据测试
DynaHOI: Benchmarking Hand-Object Interaction for Dynamic Target
- 构建在线闭环平台，用滚动评估量化动态抓取表现
- 发布1000万帧数据集，涵盖8大类22小类动态目标运动
- 提出观察-动作基线模型，位置成功率提升8.1%
现有手物交互（HOI）生成基准多聚焦静态物体，缺乏对动态目标与时间敏感协调的测试。为此，我们提出DynaHOI-Gym，一个统一的在线闭环平台，配备参数化运动生成器和基于滚动的评估指标。基于该平台，我们发布DynaHOI-10M，包含1000万帧数据与18万条手部运动轨迹，目标运动分为8大类22个细粒度子类。同时提供简单易行的观察-动作基线（ObAct），通过时空注意力融合短期观测与当前帧信息进行动作预测，使位置成功率达8.1%提升。
原文摘要 · Abstract (English)
Most existing hand motion generation benchmarks for hand-object interaction (HOI) focus on static objects, leaving dynamic scenarios with moving targets and time-critical coordination largely untested. To address this gap, we introduce the DynaHOI-Gym, a unified online closed-loop platform with parameterized motion generators and rollout-based metrics for dynamic capture evaluation. Built on DynaHOI-Gym, we release DynaHOI-10M, a large-scale benchmark with 10M frames and 180K hand capture trajectories, whose target motions are organized into 8 major categories and 22 fine-grained subcategories. We also provide a simple observe-before-act baseline (ObAct) that integrates short-term observations with the current frame via spatiotemporal attention to predict actions, achieving an 8.1% improvement in location success rate.
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