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.
@article{arxiv.2602.11919,
title = {DynaHOI: Benchmarking Hand-Object Interaction for Dynamic Target},
author = {BoCheng Hu and Zhonghan Zhao and Kaiyue Zhou and Hongwei Wang and Gaoang Wang},
journal= {arXiv preprint arXiv:2602.11919},
year = {2026}
}