Title: Epipolar Prompt: A Simple Baseline for Motion Segmentation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: motion segmentation, SAM, 3D, 4D

Abstract: Reconstructing dynamic 3D/4D scenes from uncalibrated videos remains challenging due to moving objects violating the static assumptions required for multi-view consistency. While motion segmentation can resolve this, existing methods struggle to generalize across datasets and ignore 3D geometry cues. To this end, we propose **Epipolar Prompt**, a zero-shot framework that synergizes epipolar geometry with foundation segmentation models (e.g., SAM) to achieve robust motion segmentation. Our approach first computes epipolar error maps from optical flow correspondences to localize regions that violate static scene assumptions. These error maps then guide an iterative prompt selection strategy to generate precise segmentation from SAM. Surprisingly, our simple yet effective prompt-based method outperforms both supervised and unsupervised approaches on standard benchmarks (e.g., +9.3 IoU over DAVIS2017) and demonstrates strong generalization to in-the-wild videos. Furthermore, we show that our motion masks serve as a plug-and-play enhancement for existing dynamic 4D reconstruction methods, leading to improved performance.