ControlVideo: Conditional Control for Text-driven Video Editing and Beyond
Keywords: video editing, diffusion models, text-driven editing
Abstract: This paper presents ControlVideo for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions (such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating Low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. Moreover, ControlVideo can be readily extended to the more challenging task of long video editing, where maintaining long-range temporal consistency across hundreds of frames is crucial. To achieve this, we construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by defined weight functions. Empirical results corroborate its ability to create visually realistic videos spanning hundreds of frames.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 4458
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