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In this paper, we present CCEdit, a versatile generative video editingframework based on diffusion models. Our approach employs a novel tridentnetwork structure that separates structure and appearance control, ensuringprecise and creative editing capabilities. Utilizing the foundationalControlNet architecture, we maintain the structural integrity of the videoduring editing. The incorporation of an additional appearance branch enablesusers to exert fine-grained control over the edited key frame. These two sidebranches seamlessly integrate into the main branch, which is constructed uponexisting text-to-image (T2I) generation models, through learnable temporallayers. The versatility of our framework is demonstrated through a diverserange of choices in both structure representations and personalized T2I models,as well as the option to provide the edited key frame. To facilitatecomprehensive evaluation, we introduce the BalanceCC benchmark dataset,comprising 100 videos and 4 target prompts for each video. Our extensive userstudies compare CCEdit with eight state-of-the-art video editing methods. Theoutcomes demonstrate CCEdit's substantial superiority over all other methods.

CCEdit: Creative and Controllable Video Editing via Diffusion Models