{"ID":2834673,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02006","arxiv_id":"2512.02006","title":"MV-TAP: Tracking Any Point in Multi-View Videos","abstract":"Multi-view camera systems enable rich observations of complex real-world scenes, and understanding dynamic objects in multi-view settings has become central to various applications. In this work, we present MV-TAP, a novel point tracker that tracks points across multi-view videos of dynamic scenes by leveraging cross-view information. MV-TAP utilizes camera geometry and a cross-view attention mechanism to aggregate spatio-temporal information across views, enabling more complete and reliable trajectory estimation in multi-view videos. To support this task, we construct a large-scale synthetic training dataset and real-world evaluation sets tailored for multi-view tracking. Extensive experiments demonstrate that MV-TAP outperforms existing point-tracking methods on challenging benchmarks, establishing an effective baseline for advancing research in multi-view point tracking.","short_abstract":"Multi-view camera systems enable rich observations of complex real-world scenes, and understanding dynamic objects in multi-view settings has become central to various applications. In this work, we present MV-TAP, a novel point tracker that tracks points across multi-view videos of dynamic scenes by leveraging cross-v...","url_abs":"https://arxiv.org/abs/2512.02006","url_pdf":"https://arxiv.org/pdf/2512.02006v1","authors":"[\"Jahyeok Koo\",\"Inès Hyeonsu Kim\",\"Mungyeom Kim\",\"Junghyun Park\",\"Seohyun Park\",\"Jaeyeong Kim\",\"Jung Yi\",\"Seokju Cho\",\"Seungryong Kim\"]","published":"2025-12-01T18:59:01Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}