title: GenDoP: Auto-regressive Camera Trajectory Generation as a Director of
    Photography
summary: Camera trajectory design plays a crucial role in video production, serving as
  a fundamental tool for conveying directorial intent and enhancing visual
  storytelling. In cinematography, Directors of Photography meticulously craft
  camera movements to achieve expressive and intentional framing. However,
  existing methods for camera trajectory generation remain limited: Traditional
  approaches rely on geometric optimization or handcrafted procedural systems,
  while recent learning-based methods often inherit structural biases or lack
  textual alignment, constraining creative synthesis. In this work, we introduce
  an auto-regressive model inspired by the expertise of Directors of Photography
  to generate artistic and expressive camera trajectories. We first introduce
  DataDoP, a large-scale multi-modal dataset containing 29K real-world shots with
  free-moving camera trajectories, depth maps, and detailed captions in specific
  movements, interaction with the scene, and directorial intent. Thanks to the
  comprehensive and diverse database, we further train an auto-regressive,
  decoder-only Transformer for high-quality, context-aware camera movement
  generation based on text guidance and RGBD inputs, named GenDoP. Extensive
  experiments demonstrate that compared to existing methods, GenDoP offers better
  controllability, finer-grained trajectory adjustments, and higher motion
  stability. We believe our approach establishes a new standard for
  learning-based cinematography, paving the way for future advancements in camera
  control and filmmaking. Our project website:
  https://kszpxxzmc.github.io/GenDoP/.
paper_authors._id: 67f72c452eec6ce5c8b9e8e6
paper_authors.name: Mengchen Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-10T06:39:42.813Z
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paper_authors.fullname: Zhang Mengchen
paper_authors.type: user
paper_authors.user: Dubhe-zmc
paper_authors._id: 67f72c452eec6ce5c8b9e8e7
paper_authors.name: Tong Wu
paper_authors._id: 67f72c452eec6ce5c8b9e8e8
paper_authors.name: Jing Tan
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/4baf27263841471cbd5f629a8b99424d.svg
paper_authors.fullname: Jing Tan
paper_authors.type: user
paper_authors.user: jingtan
paper_authors._id: 67f72c452eec6ce5c8b9e8e9
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 67f72c452eec6ce5c8b9e8ea
paper_authors.name: Gordon Wetzstein
paper_authors.status: admin_assigned
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paper_authors._id: 6694e583ac96ca2c17131505
paper_authors.avatarUrl: /avatars/6e7a31f257e36cf301da6f879dc0a122.svg
paper_authors.fullname: Gordon Wetzstein
paper_authors.type: user
paper_authors.user: wetzste1
paper_authors._id: 67f72c452eec6ce5c8b9e8eb
paper_authors.name: Dahua Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-10T08:00:57.092Z
paper_authors._id: 636317ed80c1a705a6eff396
paper_authors.avatarUrl: /avatars/3db090e101b916d9256d0d3e043db71d.svg
paper_authors.fullname: Dahua Lin
paper_authors.type: user
paper_authors.user: lindahua
paper_title: GenDoP: Auto-regressive Camera Trajectory Generation as a Director of
    Photography
paper_summary: Camera trajectory design plays a crucial role in video production, serving as
  a fundamental tool for conveying directorial intent and enhancing visual
  storytelling. In cinematography, Directors of Photography meticulously craft
  camera movements to achieve expressive and intentional framing. However,
  existing methods for camera trajectory generation remain limited: Traditional
  approaches rely on geometric optimization or handcrafted procedural systems,
  while recent learning-based methods often inherit structural biases or lack
  textual alignment, constraining creative synthesis. In this work, we introduce
  an auto-regressive model inspired by the expertise of Directors of Photography
  to generate artistic and expressive camera trajectories. We first introduce
  DataDoP, a large-scale multi-modal dataset containing 29K real-world shots with
  free-moving camera trajectories, depth maps, and detailed captions in specific
  movements, interaction with the scene, and directorial intent. Thanks to the
  comprehensive and diverse database, we further train an auto-regressive,
  decoder-only Transformer for high-quality, context-aware camera movement
  generation based on text guidance and RGBD inputs, named GenDoP. Extensive
  experiments demonstrate that compared to existing methods, GenDoP offers better
  controllability, finer-grained trajectory adjustments, and higher motion
  stability. We believe our approach establishes a new standard for
  learning-based cinematography, paving the way for future advancements in camera
  control and filmmaking. Our project website:
  https://kszpxxzmc.github.io/GenDoP/.
paper_ai_summary: A novel auto-regressive Transformer model, GenDoP, generates high-quality, text-guided, and context-aware camera trajectories using a large-scale multi-modal dataset, improving controllability and motion stability in cinematography.
paper_ai_keywords: DataDoP decoder-only Transformer auto-regressive model camera trajectory generation text guidance RGBD inputs motion stability controllability trajectory adjustments directorial intent