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Unveiling the Value of Motion for Cinematic Camera Trajectories — arXiv:2609.38683

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38683
License (as archived): CC0-1.0
Archived: 2026-10-07T12:44:07.276Z
SHA-256 of the archived text: 96b58cfea7a02bb110a583256d28b40914127fa4ebe60b9065af215f2359b675
Stable URL of this page: https://metraipolis.com/documento/dc_19ade030bed5

How to cite

Unveiling the Value of Motion for Cinematic Camera Trajectories — arXiv:2609.38683 — arXiv (en), https://arxiv.org/abs/2609.38683 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_19ade030bed5 (Metraipolis archive dc_19ade030bed5, sha256 96b58cfea7a02bb110a583256d28b40914127fa4ebe60b9065af215f2359b675)

BibTeX

@misc{dc19ade030bed5,
  title = {Unveiling the Value of Motion for Cinematic Camera Trajectories — arXiv:2609.38683},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38683},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_19ade030bed5; sha256 96b58cfea7a02bb110a583256d28b40914127fa4ebe60b9065af215f2359b675},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38683. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cinematic camera motion is a fundamental storytelling tool, defined not only by where the camera is positioned in the scene, but also by how it moves in terms of direction and speed. Recent work on camera trajectory generation and alignment to text relies on pose-centric representations. While in principle a network could derive direction of movement and speed, we find that in practice this might not happen. In fact, in this paper we discover that decomposing the camera trajectory representation from the traditional per-frame poses to direction and speed has surprising benefits across multiple tasks, including trajectory-to-text alignment as well as text-to-trajectory generation. To accurately evaluate the former, we introduce a simple and reliable protocol that overcomes the limitations of prior evaluation baselines. For the latter, building on this representational insight, we propose a novel generative model for camera trajectories, CineGEN, that achieves superior performance across a variety of metrics. We also propose a novel dataset, CineScript, containing movie clips that are enriched with scene descriptions as well as higher-level metadata. This novel data allows us to test models' ability to capture high-level cinematographic information. We show that, despite its simplicity, representing camera trajectories through direction and speed not only helps numerically to achieve better alignment and generation, but also inherently encodes complex directorial intent.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.38683 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:14:13.922Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).