title: AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion
    Transformers
summary: Numerous works have recently integrated 3D camera control into foundational
  text-to-video models, but the resulting camera control is often imprecise, and
  video generation quality suffers. In this work, we analyze camera motion from a
  first principles perspective, uncovering insights that enable precise 3D camera
  manipulation without compromising synthesis quality. First, we determine that
  motion induced by camera movements in videos is low-frequency in nature. This
  motivates us to adjust train and test pose conditioning schedules, accelerating
  training convergence while improving visual and motion quality. Then, by
  probing the representations of an unconditional video diffusion transformer, we
  observe that they implicitly perform camera pose estimation under the hood, and
  only a sub-portion of their layers contain the camera information. This
  suggested us to limit the injection of camera conditioning to a subset of the
  architecture to prevent interference with other video features, leading to 4x
  reduction of training parameters, improved training speed and 10% higher visual
  quality. Finally, we complement the typical dataset for camera control learning
  with a curated dataset of 20K diverse dynamic videos with stationary cameras.
  This helps the model disambiguate the difference between camera and scene
  motion, and improves the dynamics of generated pose-conditioned videos. We
  compound these findings to design the Advanced 3D Camera Control (AC3D)
  architecture, the new state-of-the-art model for generative video modeling with
  camera control.
paper_authors._id: 674d35b82636ad461d487c2c
paper_authors.name: Sherwin Bahmani
paper_authors.status: admin_assigned
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paper_authors.fullname: Sherwin Bahmani
paper_authors.type: user
paper_authors.user: sherwinbahmani
paper_authors._id: 674d35b82636ad461d487c2d
paper_authors.name: Ivan Skorokhodov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T09:45:22.220Z
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paper_authors.avatarUrl: /avatars/d7e97a16cfee39e1e50d7a5b747876f1.svg
paper_authors.fullname: Ivan Skorokhodov
paper_authors.type: user
paper_authors.user: universome
paper_authors._id: 674d35b82636ad461d487c2e
paper_authors.name: Guocheng Qian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T09:45:27.259Z
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paper_authors.fullname: Guocheng Qian
paper_authors.type: user
paper_authors.user: guochengqian
paper_authors._id: 674d35b82636ad461d487c2f
paper_authors.name: Aliaksandr Siarohin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T09:45:32.409Z
paper_authors._id: 64276311eb9a0ed86180715b
paper_authors.avatarUrl: /avatars/76f933cd549f10e5e2db379de235d304.svg
paper_authors.fullname: Aliaksandr Siarohin
paper_authors.type: user
paper_authors.user: aliaksandr-siarohin
paper_authors._id: 674d35b82636ad461d487c30
paper_authors.name: Willi Menapace
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-02T09:45:37.562Z
paper_authors._id: 6315358a362e3e95ea538081
paper_authors.avatarUrl: /avatars/3b089a25a87c2e83c6b23ccb5d2dc73e.svg
paper_authors.fullname: Willi Menapace
paper_authors.type: user
paper_authors.user: willi-menapace
paper_authors._id: 674d35b82636ad461d487c31
paper_authors.name: Andrea Tagliasacchi
paper_authors._id: 674d35b82636ad461d487c32
paper_authors.name: David B. Lindell
paper_authors._id: 674d35b82636ad461d487c33
paper_authors.name: Sergey Tulyakov
paper_title: AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion
    Transformers
paper_summary: Numerous works have recently integrated 3D camera control into foundational
  text-to-video models, but the resulting camera control is often imprecise, and
  video generation quality suffers. In this work, we analyze camera motion from a
  first principles perspective, uncovering insights that enable precise 3D camera
  manipulation without compromising synthesis quality. First, we determine that
  motion induced by camera movements in videos is low-frequency in nature. This
  motivates us to adjust train and test pose conditioning schedules, accelerating
  training convergence while improving visual and motion quality. Then, by
  probing the representations of an unconditional video diffusion transformer, we
  observe that they implicitly perform camera pose estimation under the hood, and
  only a sub-portion of their layers contain the camera information. This
  suggested us to limit the injection of camera conditioning to a subset of the
  architecture to prevent interference with other video features, leading to 4x
  reduction of training parameters, improved training speed and 10% higher visual
  quality. Finally, we complement the typical dataset for camera control learning
  with a curated dataset of 20K diverse dynamic videos with stationary cameras.
  This helps the model disambiguate the difference between camera and scene
  motion, and improves the dynamics of generated pose-conditioned videos. We
  compound these findings to design the Advanced 3D Camera Control (AC3D)
  architecture, the new state-of-the-art model for generative video modeling with
  camera control.
paper_ai_summary: Advanced 3D Camera Control (AC3D) architecture improves precise camera manipulation and video quality by optimizing pose conditioning schedules, selectively injecting camera conditioning, and using a diverse dynamic video dataset.
paper_ai_keywords: 3D camera manipulation video generation quality motion induced by camera movements pose conditioning schedules video diffusion transformer camera pose estimation camera conditioning injection AC3D architecture generative video modeling