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ControlVideo: Conditional Control for One-shot Text-driven Video Editing and Beyond (2305.17098v2)

Published 26 May 2023 in cs.CV

Abstract: This paper presents \emph{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. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing (e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous works achieved. The code is available at \href{https://github.com/thu-ml/controlvideo}{https://github.com/thu-ml/controlvideo} and the visualization results are available at \href{https://drive.google.com/file/d/1wEgc2io3UwmoC5vTPbkccFvTkwVqsZlK/view?usp=drive_link}{HERE}.

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Citations (1)
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Summary

  • The paper introduces ControlVideo, a framework that integrates visual conditions and key-frame attention into diffusion models for precise one-shot text-driven video editing.
  • It segments long videos into overlapping parts that are independently processed and then fused, ensuring both local and global temporal consistency.
  • Empirical results show enhanced SSIM, CLIP-temp, and CLIP-text metrics, confirming high-fidelity video generation and robust text alignment.

An Analysis of "ControlVideo: Conditional Control for One-shot Text-driven Video Editing and Beyond"

The paper presents a sophisticated framework named ControlVideo, designed for one-shot text-driven video editing. This work builds on the capabilities of pre-trained text-to-image (T2I) diffusion models to achieve high-fidelity and temporally consistent video modifications that align closely with textual prompts and source videos.

Key Contributions

The authors have implemented several enhancements to the standard T2I diffusion model. These include:

  • Incorporation of Visual Controls: ControlVideo integrates additional visual conditions, such as edge maps and depth data, to enhance content faithfulness from source videos. This adaptation employs ControlNet, which processes these conditions in tandem with the main UNet of the diffusion model.
  • Key-frame and Temporal Attention: The paper discusses the formulation of key-frame attention, where all video frames are adjusted relative to a reference frame, aimed at preserving temporal consistency. Additionally, temporal attention modules are introduced to capture spatial relationships across frame sequences. These modules are initialized from pre-existing self-attention weights of the T2I diffusion models, ensuring efficient learning while preserving model integrity.
  • Extension to Long Video Editing: Recognizing the intrinsic limitations of diffusion models regarding memory when handling lengthy videos, ControlVideo incorporates a novel mechanism. It divides videos into overlapping segments which are processed independently, followed by a systematic fusion using predefined weight functions. This technique guarantees local and global temporal consistency over extended durations.

Empirical Findings

ControlVideo's performance surpasses existing methods on standard evaluation metrics:

  • High-Fidelity Generation: The model achieved superior results in maintaining the identity and dynamics of source video content, evidenced through qualitative analyses and SSIM measurements.
  • Temporal Consistency: Enhanced temporal consistency was quantified using CLIP-temp metrics, with ControlVideo outperforming competitive baselines in maintaining coherent temporal transitions across edited frames.
  • Text Alignment and Conditional Flexibility: Equipped with multi-condition support, ControlVideo efficiently matches the generated video content to the guiding text, confirmed through CLIP-text metrics and user studies.

Implications and Future Directions

The implications of this research span both theoretical advancements and practical applications. Theoretically, the integration of visual conditions and attention mechanisms could inspire future research in merging different modalities to enhance the generative precision of diffusion models. Practically, ControlVideo's capacity to handle long-form video edits with high fidelity promises substantial utility in industries reliant on video content generation, such as marketing and entertainment.

Looking ahead, the exploration of more sophisticated fusion techniques and weight functions could further improve performance in long video editing. Moreover, extending the framework to handle more complex multi-modal tasks, such as real-time video processing in dynamic environments, could be a noteworthy avenue for future research in AI-driven content creation.

In summary, ControlVideo provides a robust framework capable of leveraging existing diffusion model architectures for nuanced and complex video editing tasks, setting a valuable precedent for further studies in the integration of AI with creative processes.

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