MolmoMotion: Language-guided 3D motion forecasting

MolmoMotion is an advanced 3D motion forecasting model designed to predict future trajectories of objects in 3D space using video frames, 3D points, and language instructions, outperforming existing methods. By representing motion as object-attached 3D points, the model achieves efficient and accurate predictions across various scenarios, making it useful for applications such as robotics planning and video generation. The model is trained on MolmoMotion-1M, the largest dataset of 3D point trajectories paired with action descriptions, and evaluated using PointMotionBench, a benchmark for measuring 3D motion forecasting accuracy. MolmoMotion employs two variants: autoregressive for smooth and accurate predictions and flow-matching for handling uncertainty, allowing it to adapt to different downstream tasks. Despite its promising capabilities, the model has limitations in handling complex deformable motions due to the sparse query points used during training. Nonetheless, MolmoMotion represents a significant step toward anticipating object movements, offering potential applications beyond perception in fields like robotics and video generation, and encourages further exploration and customization by the community.

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