FancyVideo: Towards Dynamic and Consistent Video Generation via
  Cross-frame Textual Guidance
Authors: name: Jiasong Feng, name: Ao Ma, name: Jing Wang, name: Bo Cheng, name: Xiaodan Liang, name: Dawei Leng, name: Yuhui Yin

Synthesizing motion-rich and temporally consistent videos remains a challenge
in artificial intelligence, especially when dealing with extended durations.
Existing text-to-video (T2V) models commonly employ spatial cross-attention for
text control, equivalently guiding different frame generations without
frame-specific textual guidance. Thus, the model's capacity to comprehend the
temporal logic conveyed in prompts and generate videos with coherent motion is
restricted. To tackle this limitation, we introduce FancyVideo, an innovative
video generator that improves the existing text-control mechanism with the
well-designed Cross-frame Textual Guidance Module (CTGM). Specifically, CTGM
incorporates the Temporal Information Injector (TII), Temporal Affinity Refiner
(TAR), and Temporal Feature Booster (TFB) at the beginning, middle, and end of
cross-attention, respectively, to achieve frame-specific textual guidance.
Firstly, TII injects frame-specific information from latent features into text
conditions, thereby obtaining cross-frame textual conditions. Then, TAR refines
the correlation matrix between cross-frame textual conditions and latent
features along the time dimension. Lastly, TFB boosts the temporal consistency
of latent features. Extensive experiments comprising both quantitative and
qualitative evaluations demonstrate the effectiveness of FancyVideo. Our
approach achieves state-of-the-art T2V generation results on the EvalCrafter
benchmark and facilitates the synthesis of dynamic and consistent videos. The
video show results can be available at https://fancyvideo.github.io/, and we
will make our code and model weights publicly available.