  Research on diffusion model-based video generation has advanced rapidly.
However, limitations in object fidelity and generation length hinder its
practical applications. Additionally, specific domains like animated wallpapers
require seamless looping, where the first and last frames of the video match
seamlessly. To address these challenges, this paper proposes LoopAnimate, a
novel method for generating videos with consistent start and end frames. To
enhance object fidelity, we introduce a framework that decouples multi-level
image appearance and textual semantic information. Building upon an
image-to-image diffusion model, our approach incorporates both pixel-level and
feature-level information from the input image, injecting image appearance and
textual semantic embeddings at different positions of the diffusion model.
Existing UNet-based video generation models require to input the entire videos
during training to encode temporal and positional information at once. However,
due to limitations in GPU memory, the number of frames is typically restricted
to 16. To address this, this paper proposes a three-stage training strategy
with progressively increasing frame numbers and reducing fine-tuning modules.
Additionally, we introduce the Temporal E nhanced Motion Module(TEMM) to extend
the capacity for encoding temporal and positional information up to 36 frames.
The proposed LoopAnimate, which for the first time extends the single-pass
generation length of UNet-based video generation models to 35 frames while
maintaining high-quality video generation. Experiments demonstrate that
LoopAnimate achieves state-of-the-art performance in both objective metrics,
such as fidelity and temporal consistency, and subjective evaluation results.
