GeoMotion：以潜空间4D几何重新审视运动分割
GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry
用4D场景几何重建（如π³）提供的可靠相机位姿与时空先验，替代显式对应估计，通过注意力在潜特征上隐式解耦物体运动与相机运动，为动态场景运动分割提供免迭代、免预处理的前馈范式。
原文依据 bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论动态场景中的运动分割极具挑战性，因为传统方法严重依赖从本身含噪的运动线索中估计相机位姿与点对应关系。现有的统计推断或迭代优化技术难以抑制多阶段流水线中的累积误差，往往导致性能受限或计算开销高昂。与之相反，我们提出一种完全基于学习的方法，通过注意力机制直接从潜特征表示中推断运动物体，从而实现端到端的前馈运动分割。我们的核心洞见是绕过显式的对应估计，转而让模型学习隐式地解耦物体运动与相机运动。在4D场景几何重建（如π³）近期进展的支持下，所提方法利用可靠的相机位姿与丰富的时空先验，确保模型训练的稳定与推理的鲁棒。大量实验表明，通过消除复杂的预处理与迭代精化，我们的方法以高效率实现了最优的运动分割性能。代码见：https://github.com/zjutcvg/GeoMotion。
英文原文摘要 1239 字符
Motion segmentation in dynamic scenes is highly challenging, as conventional methods heavily rely on estimating camera poses and point correspondences from inherently noisy motion cues. Existing statistical inference or iterative optimization techniques that struggle to mitigate the cumulative errors in multi-stage pipelines often lead to limited performance or high computational cost. In contrast, we propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms, thus enabling end-to-end feed-forward motion segmentation. Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion. Supported by recent advances in 4D scene geometry reconstruction (e.g., \pi^3), the proposed method leverages reliable camera poses and rich spatial-temporal priors, which ensure stable training and robust inference for the model. Extensive experiments demonstrate that by eliminating complex pre-processing and iterative refinement, our approach achieves state-of-the-art motion segmentation performance with high efficiency. The code is available at:https://github.com/zjutcvg/GeoMotion.