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CCEdit: Creative and Controllable Video Editing via Diffusion Models

Ruoyu Feng · Wenming Weng · Yanhui Wang · Yuhui Yuan · Jianmin Bao · Chong Luo · Zhibo Chen · Baining Guo

Arch 4A-E Poster #186
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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. 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, affirming its exceptional editing capability.

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