HuMo3D: Intention-Driven Dual-Branch Multimodal Human Motion Prediction in 3D Scenes

Human motion prediction is a fundamental task for enabling intelligent systems to anticipate human behavior in dynamic environments. Despite significant progress, existing methods primarily focus on human-object interactions, often neglecting the underlying human intention, which results in predictions that violate real-world spatial and semantic constraints. In this paper, we propose HuMo3D, a dual-branch multimodal framework that explicitly integrates human intention with scene geometry. Our framework introduces two key components: the Trajectory Intention Learner, which captures the relationship between human motion and scene geometry, and the Pose Intention Learner, which dynamically prioritizes potential interaction regions to model pose-intention relationships. Extensive experiments on the MoGaze and GIMO benchmark dataset show that HuMo3D significantly outperforms state-of-the-art motion prediction methods, highlighting the importance of intention-aware modeling in 3D human motion prediction. For more details and code, please refer to the project page.