MV-TAP: Tracking Any Point in Multi-View Videos

• URL: http://arxiv.org/abs/2512.02006v1
• Date: Mon, 01 Dec 2025 18:59:01 GMT
• Title: MV-TAP: Tracking Any Point in Multi-View Videos
• Authors: Jahyeok Koo, Inès Hyeonsu Kim, Mungyeom Kim, Junghyun Park, Seohyun Park, Jaeyeong Kim, Jung Yi, Seokju Cho, Seungryong Kim,
• Abstract summary: MV-TAP is a novel point tracker that tracks points across multi-view videos of dynamic scenes by leveraging cross-view information.<n>To support this task, we construct a large-scale synthetic training dataset and real-world evaluation sets tailored for multi-view tracking.
• Score: 34.91357343992975
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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