Mobius: Text to Seamless Looping Video Generation via Latent Shift
Authors: name: Xiuli Bi, name: Jianfei Yuan, name: Bo Liu, name: Yong Zhang, name: Xiaodong Cun, name: Chi-Man Pun, name: Bin Xiao

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.