title: Ouroboros-Diffusion: Exploring Consistent Content Generation in
    Tuning-free Long Video Diffusion
summary: The first-in-first-out (FIFO) video diffusion, built on a pre-trained
  text-to-video model, has recently emerged as an effective approach for
  tuning-free long video generation. This technique maintains a queue of video
  frames with progressively increasing noise, continuously producing clean frames
  at the queue's head while Gaussian noise is enqueued at the tail. However,
  FIFO-Diffusion often struggles to keep long-range temporal consistency in the
  generated videos due to the lack of correspondence modeling across frames. In
  this paper, we propose Ouroboros-Diffusion, a novel video denoising framework
  designed to enhance structural and content (subject) consistency, enabling the
  generation of consistent videos of arbitrary length. Specifically, we introduce
  a new latent sampling technique at the queue tail to improve structural
  consistency, ensuring perceptually smooth transitions among frames. To enhance
  subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA)
  mechanism, which aligns subjects across frames within short segments to achieve
  better visual coherence. Furthermore, we introduce self-recurrent guidance.
  This technique leverages information from all previous cleaner frames at the
  front of the queue to guide the denoising of noisier frames at the end,
  fostering rich and contextual global information interaction. Extensive
  experiments of long video generation on the VBench benchmark demonstrate the
  superiority of our Ouroboros-Diffusion, particularly in terms of subject
  consistency, motion smoothness, and temporal consistency.
paper_authors._id: 678893e40a465aa0613dc9b2
paper_authors.name: Jingyuan Chen
paper_authors._id: 678893e40a465aa0613dc9b3
paper_authors.name: Fuchen Long
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-16T09:41:40.611Z
paper_authors._id: 6449f2dfeb7db8f70fb990f8
paper_authors.avatarUrl: /avatars/72c02e754bb35dab05c4a5f1e69c95f1.svg
paper_authors.fullname: Fuchen
paper_authors.type: user
paper_authors.user: FireCRT
paper_authors._id: 678893e40a465aa0613dc9b4
paper_authors.name: Jie An
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-17T10:25:29.353Z
paper_authors._id: 6267fb1387d7f9040e031d39
paper_authors.avatarUrl: /avatars/c7170aa3d28d2cb851e075b98ca5f5b1.svg
paper_authors.fullname: Jie An
paper_authors.type: user
paper_authors.user: pkuanjie
paper_authors._id: 678893e40a465aa0613dc9b5
paper_authors.name: Zhaofan Qiu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T08:54:51.062Z
paper_authors._id: 64ac099294ae6b609d0f0713
paper_authors.avatarUrl: /avatars/7f50e3e1597fb5fd0dad9fe7fd2e26ec.svg
paper_authors.fullname: Zhaofan Qiu
paper_authors.type: user
paper_authors.user: qiudavy
paper_authors._id: 678893e40a465aa0613dc9b6
paper_authors.name: Ting Yao
paper_authors._id: 678893e40a465aa0613dc9b7
paper_authors.name: Jiebo Luo
paper_authors._id: 678893e40a465aa0613dc9b8
paper_authors.name: Tao Mei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T08:55:14.020Z
paper_authors._id: 66a8d386fbde2b9cb1120393
paper_authors.avatarUrl: /avatars/ccfb6e72764e547be2b3528713151693.svg
paper_authors.fullname: Tao Mei
paper_authors.type: user
paper_authors.user: GiantBision
paper_title: Ouroboros-Diffusion: Exploring Consistent Content Generation in
    Tuning-free Long Video Diffusion
paper_summary: The first-in-first-out (FIFO) video diffusion, built on a pre-trained
  text-to-video model, has recently emerged as an effective approach for
  tuning-free long video generation. This technique maintains a queue of video
  frames with progressively increasing noise, continuously producing clean frames
  at the queue's head while Gaussian noise is enqueued at the tail. However,
  FIFO-Diffusion often struggles to keep long-range temporal consistency in the
  generated videos due to the lack of correspondence modeling across frames. In
  this paper, we propose Ouroboros-Diffusion, a novel video denoising framework
  designed to enhance structural and content (subject) consistency, enabling the
  generation of consistent videos of arbitrary length. Specifically, we introduce
  a new latent sampling technique at the queue tail to improve structural
  consistency, ensuring perceptually smooth transitions among frames. To enhance
  subject consistency, we devise a Subject-Aware Cross-Frame Attention (SACFA)
  mechanism, which aligns subjects across frames within short segments to achieve
  better visual coherence. Furthermore, we introduce self-recurrent guidance.
  This technique leverages information from all previous cleaner frames at the
  front of the queue to guide the denoising of noisier frames at the end,
  fostering rich and contextual global information interaction. Extensive
  experiments of long video generation on the VBench benchmark demonstrate the
  superiority of our Ouroboros-Diffusion, particularly in terms of subject
  consistency, motion smoothness, and temporal consistency.
paper_ai_keywords: FIFO-Diffusion dense text-to-video model video frames Gaussian noise latent sampling technique Ouroboros-Diffusion video denoising framework SubjectSubject-Aware Cross-Attention (SACFA) self-recurrent guidance VBench benchmark perceptually smooth transitions visual coherence motion smoothness temporal consistency