title: Training-free Camera Control for Video Generation
summary: We propose a training-free and robust solution to offer camera movement
  control for off-the-shelf video diffusion models. Unlike previous work, our
  method does not require any supervised finetuning on camera-annotated datasets
  or self-supervised training via data augmentation. Instead, it can be plugged
  and played with most pretrained video diffusion models and generate camera
  controllable videos with a single image or text prompt as input. The
  inspiration of our work comes from the layout prior that intermediate latents
  hold towards generated results, thus rearranging noisy pixels in them will make
  output content reallocated as well. As camera move could also be seen as a kind
  of pixel rearrangement caused by perspective change, videos could be
  reorganized following specific camera motion if their noisy latents change
  accordingly. Established on this, we propose our method CamTrol, which enables
  robust camera control for video diffusion models. It is achieved by a two-stage
  process. First, we model image layout rearrangement through explicit camera
  movement in 3D point cloud space. Second, we generate videos with camera motion
  using layout prior of noisy latents formed by a series of rearranged images.
  Extensive experiments have demonstrated the robustness our method holds in
  controlling camera motion of generated videos. Furthermore, we show that our
  method can produce impressive results in generating 3D rotation videos with
  dynamic content. Project page at https://lifedecoder.github.io/CamTrol/.
paper_authors._id: 666fd85c3666df05b7b4f569
paper_authors.name: Chen Hou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-17T07:31:15.600Z
paper_authors._id: 660a367942025e0a8b8a2de5
paper_authors.avatarUrl: /avatars/7a79a12d9f67100b0ea2dfb19dc695d0.svg
paper_authors.fullname: Chen Hou
paper_authors.type: user
paper_authors.user: LegolasS
paper_authors._id: 666fd85c3666df05b7b4f56a
paper_authors.name: Guoqiang Wei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-17T07:31:27.898Z
paper_authors._id: 63160a1d29411a6864b0e585
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1668083049513-63160a1d29411a6864b0e585.jpeg
paper_authors.fullname: Guoqiang Wei
paper_authors.type: user
paper_authors.user: weigq
paper_authors._id: 666fd85c3666df05b7b4f56b
paper_authors.name: Yan Zeng
paper_authors._id: 666fd85c3666df05b7b4f56c
paper_authors.name: Zhibo Chen
paper_title: Training-free Camera Control for Video Generation
paper_summary: We propose a training-free and robust solution to offer camera movement
  control for off-the-shelf video diffusion models. Unlike previous work, our
  method does not require any supervised finetuning on camera-annotated datasets
  or self-supervised training via data augmentation. Instead, it can be plugged
  and played with most pretrained video diffusion models and generate camera
  controllable videos with a single image or text prompt as input. The
  inspiration of our work comes from the layout prior that intermediate latents
  hold towards generated results, thus rearranging noisy pixels in them will make
  output content reallocated as well. As camera move could also be seen as a kind
  of pixel rearrangement caused by perspective change, videos could be
  reorganized following specific camera motion if their noisy latents change
  accordingly. Established on this, we propose our method CamTrol, which enables
  robust camera control for video diffusion models. It is achieved by a two-stage
  process. First, we model image layout rearrangement through explicit camera
  movement in 3D point cloud space. Second, we generate videos with camera motion
  using layout prior of noisy latents formed by a series of rearranged images.
  Extensive experiments have demonstrated the robustness our method holds in
  controlling camera motion of generated videos. Furthermore, we show that our
  method can produce impressive results in generating 3D rotation videos with
  dynamic content. Project page at https://lifedecoder.github.io/CamTrol/.
paper_ai_summary: A training-free method, CamTrol, enables robust camera control in video diffusion models using a two-stage process involving 3D point cloud space and image layout rearrangement.
paper_ai_keywords: video diffusion models camera movement control layout prior noisy latents 3D point cloud space 3D rotation videos dynamic content