title: CameraCtrl: Enabling Camera Control for Text-to-Video Generation
summary: Controllability plays a crucial role in video generation since it allows
  users to create desired content. However, existing models largely overlooked
  the precise control of camera pose that serves as a cinematic language to
  express deeper narrative nuances. To alleviate this issue, we introduce
  CameraCtrl, enabling accurate camera pose control for text-to-video(T2V)
  models. After precisely parameterizing the camera trajectory, a plug-and-play
  camera module is then trained on a T2V model, leaving others untouched.
  Additionally, a comprehensive study on the effect of various datasets is also
  conducted, suggesting that videos with diverse camera distribution and similar
  appearances indeed enhance controllability and generalization. Experimental
  results demonstrate the effectiveness of CameraCtrl in achieving precise and
  domain-adaptive camera control, marking a step forward in the pursuit of
  dynamic and customized video storytelling from textual and camera pose inputs.
  Our project website is at: https://hehao13.github.io/projects-CameraCtrl/.
paper_authors._id: 660cb980062846c7a884bf2e
paper_authors.name: Hao He
paper_authors._id: 660cb980062846c7a884bf2f
paper_authors.name: Yinghao Xu
paper_authors._id: 660cb980062846c7a884bf30
paper_authors.name: Yuwei Guo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-16T21:24:07.347Z
paper_authors._id: 6371f83d5ffa922d638ef486
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6371f83d5ffa922d638ef486/UzWl3xhVvtol3Rz1mjQ9t.jpeg
paper_authors.fullname: Yuwei Guo
paper_authors.type: user
paper_authors.user: guoyww
paper_authors._id: 660cb980062846c7a884bf31
paper_authors.name: Gordon Wetzstein
paper_authors._id: 660cb980062846c7a884bf32
paper_authors.name: Bo Dai
paper_authors._id: 660cb980062846c7a884bf33
paper_authors.name: Hongsheng Li
paper_authors._id: 660cb980062846c7a884bf34
paper_authors.name: Ceyuan Yang
paper_title: CameraCtrl: Enabling Camera Control for Text-to-Video Generation
paper_summary: Controllability plays a crucial role in video generation since it allows
  users to create desired content. However, existing models largely overlooked
  the precise control of camera pose that serves as a cinematic language to
  express deeper narrative nuances. To alleviate this issue, we introduce
  CameraCtrl, enabling accurate camera pose control for text-to-video(T2V)
  models. After precisely parameterizing the camera trajectory, a plug-and-play
  camera module is then trained on a T2V model, leaving others untouched.
  Additionally, a comprehensive study on the effect of various datasets is also
  conducted, suggesting that videos with diverse camera distribution and similar
  appearances indeed enhance controllability and generalization. Experimental
  results demonstrate the effectiveness of CameraCtrl in achieving precise and
  domain-adaptive camera control, marking a step forward in the pursuit of
  dynamic and customized video storytelling from textual and camera pose inputs.
  Our project website is at: https://hehao13.github.io/projects-CameraCtrl/.
paper_ai_keywords: CameraCtrl text-to-video (T2V) camera trajectory plug-and-play camera module controllability generalization camera distribution domain-adaptive dynamic and customized video storytelling textual and camera pose inputs