Mobius: Text to Seamless Looping Video Generation via Latent Shift
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.
67c1460201cef6d4b9b9ac73
Xiuli Bi
67c1460201cef6d4b9b9ac74
Jianfei Yuan
67c1460201cef6d4b9b9ac75
Bo Liu
67c1460201cef6d4b9b9ac76
Yong Zhang
67c1460201cef6d4b9b9ac77
Xiaodong Cun
admin_assigned
2025-02-28T12:38:07.038Z
63184c517ca1b876d99b7e0e
https://cdn-avatars.huggingface.co/v1/production/uploads/63184c517ca1b876d99b7e0e/b-qDExoeJuDXK0cJBZKnz.jpeg
Xiaodong Cun
user
vinthony
67c1460201cef6d4b9b9ac78
Chi-Man Pun
67c1460201cef6d4b9b9ac79
Bin Xiao
Mobius: Text to Seamless Looping Video Generation via Latent Shift
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.
A novel method for generating looping videos from text using pre-trained video latent diffusion models without user annotations, ensuring temporal consistency and enabling dynamic motion.
video latent diffusion model latent cycle multi-frame latent denoising temporal consistency context latent-shifting approach