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I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models (2405.16537v1)

Published 26 May 2024 in cs.CV

Abstract: The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the development of more diverse, high-quality approaches and more capable software like Photoshop. In light of this gap, we introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model. Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits, effectively handling global edits, local edits, and moderate shape changes, which existing methods cannot fully achieve. At the core of our method are two main processes: Coarse Motion Extraction to align basic motion patterns with the original video, and Appearance Refinement for precise adjustments using fine-grained attention matching. We also incorporate a skip-interval strategy to mitigate quality degradation from auto-regressive generation across multiple video clips. Experimental results demonstrate our framework's superior performance in fine-grained video editing, proving its capability to produce high-quality, temporally consistent outputs.

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First-Frame-Guided Video Editing via Image-to-Video Diffusion Models

The paper presents I2VEdit, an innovative framework designed for video editing by leveraging state-of-the-art diffusion models that extend the capability of image editing tools to videos. The proposed method is significant in addressing the salient issues inherent in video editing, such as maintaining temporal consistency and cross-frame spatial correlations, by efficiently propagating edits made to the first frame of a video throughout the entire sequence. This is an accomplished effort to bridge the quality and flexibility gap between image editing and video editing.

Methodology

I2VEdit operates in two main stages: Coarse Motion Extraction and Appearance Refinement. Below is a detailed examination of these stages:

  1. Coarse Motion Extraction:
    • Motion LoRA: This framework component focuses on fine-tuning the pre-trained image-to-video model by adding low-rank adaptation (LoRA) layers to temporal attention layers, effectively aligning the video generation with the coarse motion patterns of the source video clip.
    • Skip-Interval Cross-Attention: To address potential degradation in quality due to the autoregressive generation strategy, skip-interval cross-attention is introduced. This mechanism ensures that temporal self-attention aligns the appearance features across video clips by concatenating key and value matrices with those from the initial segment, thereby enhancing overall temporal coherence.
  2. Appearance Refinement:
    • Smooth Area Random Perturbation (SARP): This technique improves the quality of latents derived from EDM inversion, particularly when the source video contains large smooth areas with constant pixel values. By introducing minor perturbations, SARP ensures these latents conform to expected Gaussian distributions, which is crucial for maintaining interactive editing quality during the denoising process.
    • Fine-Grained Attention Matching: During the refinement phase, both spatial and temporal attentions are meticulously adjusted. The method utilizes spatial attention difference maps to gauge and adapt to the extent of edits, ensuring precise motion and appearance consistency. Temporal attentions are matched in a staged approach to balance the trade-offs between preserving detailed edits and maintaining original motion trajectories.

Experimental Validation

The paper presents comprehensive experiments comparing I2VEdit with other state-of-the-art methods including Ebsynth, AnyV2V, and leading text-guided video editing methodologies such as FateZero, Rerender-A-Video, and VMC. The experimental results substantiate that I2VEdit remarkably outperforms these methods, particularly in tasks requiring fine-grained local edits. This is evident from both qualitative visual assessments and quantitative measures.

Key aspects evaluated include:

  • Motion Preservation (MP): I2VEdit exhibits superior performance in maintaining the original motion dynamics of the video, as compared to other techniques.
  • Appearance Alignment (AA): For unedited areas, the method maintains high fidelity to the source video’s appearance.
  • Editing Quality (EQ): The overall quality of edits, particularly in challenging local editing scenarios, is enhanced.
  • Temporal Consistency (TC): Temporal coherence of the generated video frames is significantly better, reducing noticeable artifactual transitions between frames.
  • Appearance Consistency (AC): The edited frames remain consistent with the initial frame’s appearance across the video sequence.

Human and automatic evaluations are performed, which collectively endorse the efficacy of I2VEdit in producing high-quality, temporally consistent video outputs.

Implications and Future Directions

The development of I2VEdit holds substantial implications for both practical applications and theoretical advancements:

  1. Practical Implications:
    • Enhanced Flexibility: Users can now employ sophisticated image editing tools to make precise edits which are then effectively propagated throughout a video, significantly simplifying the video editing process.
    • Efficiency: By separating content editing and motion preservation tasks, the proposed framework successfully reduces the computational and manual workload traditionally associated with video editing.
  2. Theoretical Implications:
    • Robust Diffusion Models: The successful adaptation of image-to-video diffusion models underscores their robustness and potential for further research in video synthesis and editing.
    • Improved Attention Mechanisms: Innovations like fine-grained attention matching and skip-interval cross-attention provide valuable insights into handling temporal coherence and appearance consistency, potentially benefiting other domains employing attention-based methods.

Looking forward, further research could explore:

  • Extended Video Lengths: Optimizations that facilitate handling longer video sequences without quality loss.
  • Incorporation of User Feedback: Interactive models that can adapt edits based on real-time user inputs.
  • Generalization Across Diverse Content: Enhancing the framework’s adaptability to various video genres and scene complexities.

In conclusion, the I2VEdit framework represents a significant contribution to the field of video editing, facilitating sophisticated, high-quality edits with enhanced temporal coherence. This work not only advances current methodologies but also sets a foundation for future innovations in leveraging diffusion models for video content creation and modification.

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Authors (5)
  1. Wenqi Ouyang (5 papers)
  2. Yi Dong (46 papers)
  3. Lei Yang (372 papers)
  4. Jianlou Si (8 papers)
  5. Xingang Pan (45 papers)
Citations (2)
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