title: Mobius: Text to Seamless Looping Video Generation via Latent Shift
summary: We present Mobius, a novel method to generate seamlessly looping videos from
  text descriptions directly without any user annotations, thereby creating new
  visual materials for the multi-media presentation. Our method repurposes the
  pre-trained video latent diffusion model for generating looping videos from
  text prompts without any training. During inference, we first construct a
  latent cycle by connecting the starting and ending noise of the videos. Given
  that the temporal consistency can be maintained by the context of the video
  diffusion model, we perform multi-frame latent denoising by gradually shifting
  the first-frame latent to the end in each step. As a result, the denoising
  context varies in each step while maintaining consistency throughout the
  inference process. Moreover, the latent cycle in our method can be of any
  length. This extends our latent-shifting approach to generate seamless looping
  videos beyond the scope of the video diffusion model's context. Unlike previous
  cinemagraphs, the proposed method does not require an image as appearance,
  which will restrict the motions of the generated results. Instead, our method
  can produce more dynamic motion and better visual quality. We conduct multiple
  experiments and comparisons to verify the effectiveness of the proposed method,
  demonstrating its efficacy in different scenarios. All the code will be made
  available.
paper_authors._id: 67c1460201cef6d4b9b9ac73
paper_authors.name: Xiuli Bi
paper_authors._id: 67c1460201cef6d4b9b9ac74
paper_authors.name: Jianfei Yuan
paper_authors._id: 67c1460201cef6d4b9b9ac75
paper_authors.name: Bo Liu
paper_authors._id: 67c1460201cef6d4b9b9ac76
paper_authors.name: Yong Zhang
paper_authors._id: 67c1460201cef6d4b9b9ac77
paper_authors.name: Xiaodong Cun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:38:07.038Z
paper_authors._id: 63184c517ca1b876d99b7e0e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63184c517ca1b876d99b7e0e/b-qDExoeJuDXK0cJBZKnz.jpeg
paper_authors.fullname: Xiaodong Cun
paper_authors.type: user
paper_authors.user: vinthony
paper_authors._id: 67c1460201cef6d4b9b9ac78
paper_authors.name: Chi-Man Pun
paper_authors._id: 67c1460201cef6d4b9b9ac79
paper_authors.name: Bin Xiao
paper_title: Mobius: Text to Seamless Looping Video Generation via Latent Shift
paper_summary: We present Mobius, a novel method to generate seamlessly looping videos from
  text descriptions directly without any user annotations, thereby creating new
  visual materials for the multi-media presentation. Our method repurposes the
  pre-trained video latent diffusion model for generating looping videos from
  text prompts without any training. During inference, we first construct a
  latent cycle by connecting the starting and ending noise of the videos. Given
  that the temporal consistency can be maintained by the context of the video
  diffusion model, we perform multi-frame latent denoising by gradually shifting
  the first-frame latent to the end in each step. As a result, the denoising
  context varies in each step while maintaining consistency throughout the
  inference process. Moreover, the latent cycle in our method can be of any
  length. This extends our latent-shifting approach to generate seamless looping
  videos beyond the scope of the video diffusion model's context. Unlike previous
  cinemagraphs, the proposed method does not require an image as appearance,
  which will restrict the motions of the generated results. Instead, our method
  can produce more dynamic motion and better visual quality. We conduct multiple
  experiments and comparisons to verify the effectiveness of the proposed method,
  demonstrating its efficacy in different scenarios. All the code will be made
  available.
paper_ai_keywords: video diffusion model latent cycle latent cycle latent denoising temporal consistency context of the video diffusion model multi-frame latent denoising latent-shifting approach seamless looping videos generated visuals video diffusion model latent cycle latent denoising context of the video diffusion model multi-frame latent denoising latent-shifting approach