Intend to Move: A Dataset for Intention and Scene Aware Human Motion Prediction
Ryo Umagami, Liu Yue, Xuangeng Chu, Ryuto Fukushima, Tetsuya Narita, Yusuke Mukuta, Tomoyuki Takahata, Jianfei Yang, Tatsuya Harada
Humans move with intention. However, existing human motion datasets and prediction models typically focus on short-term kinematics and lack explicit representations of high-level goals or social context. In this work, we introduce \textit{Intend to Move (I2M)}, a new dataset designed for intention-aware long-term human motion modeling in dynamic real-world environments. I2M contains 10.1 hours of SMPL-based 3D motion across 215 sequences, each synchronized with multi-view RGB-D video, 3D scene point clouds, and natural language descriptions of human intentions. We conduct a comprehensive analysis across eight conditioning modalities, including trajectories, semantic features, and multimodal combinations. Our results show that structured motion cues (e.g., joint trajectories) yield superior prediction accuracy, while high-level semantics such as intention text remain underutilized by current pretrained encoders. This highlights a significant gap between semantic intention representations and their grounding in physical motion. We present I2M as a benchmark to promote future research in bridging this gap through intention-grounded learning for embodied intelligence.