  In this paper, we present CCEdit, a versatile generative video editing
framework based on diffusion models. Our approach employs a novel trident
network structure that separates structure and appearance control, ensuring
precise and creative editing capabilities. Utilizing the foundational
ControlNet architecture, we maintain the structural integrity of the video
during editing. The incorporation of an additional appearance branch enables
users to exert fine-grained control over the edited key frame. These two side
branches seamlessly integrate into the main branch, which is constructed upon
existing text-to-image (T2I) generation models, through learnable temporal
layers. The versatility of our framework is demonstrated through a diverse
range of choices in both structure representations and personalized T2I models,
as well as the option to provide the edited key frame. To facilitate
comprehensive evaluation, we introduce the BalanceCC benchmark dataset,
comprising 100 videos and 4 target prompts for each video. Our extensive user
studies compare CCEdit with eight state-of-the-art video editing methods. The
outcomes demonstrate CCEdit's substantial superiority over all other methods.
