DynEoMT: Learning Object Dynamicity from Online Segmentation Queries
Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the observing camera. This dynamicity attribute cannot be inferred from semantics alone and is confounded by camera ego-motion. We introduce \method, an online framework that augments query-based video segmentation with region-level dynamicity prediction. It jointly produces the original segmentation outputs and a dynamic or static state for each predicted region. At inference, DynEoMT uses only the current frame and propagated queries, without optical flow, depth, camera pose, previous RGB frames, or feature maps. Because established video segment...
| Authors | Calvin Galagain, Martyna Poreba, François Goulette |
|---|---|
| Primary category | cs.CV |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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