ReVideo: Remake a Video with Motion and Content Control
(2405.13865)Abstract
Despite significant advancements in video generation and editing using diffusion models, achieving accurate and localized video editing remains a substantial challenge. Additionally, most existing video editing methods primarily focus on altering visual content, with limited research dedicated to motion editing. In this paper, we present a novel attempt to Remake a Video (ReVideo) which stands out from existing methods by allowing precise video editing in specific areas through the specification of both content and motion. Content editing is facilitated by modifying the first frame, while the trajectory-based motion control offers an intuitive user interaction experience. ReVideo addresses a new task involving the coupling and training imbalance between content and motion control. To tackle this, we develop a three-stage training strategy that progressively decouples these two aspects from coarse to fine. Furthermore, we propose a spatiotemporal adaptive fusion module to integrate content and motion control across various sampling steps and spatial locations. Extensive experiments demonstrate that our ReVideo has promising performance on several accurate video editing applications, i.e., (1) locally changing video content while keeping the motion constant, (2) keeping content unchanged and customizing new motion trajectories, (3) modifying both content and motion trajectories. Our method can also seamlessly extend these applications to multi-area editing without specific training, demonstrating its flexibility and robustness.
Overview
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ReVideo introduces a novel approach to video editing that allows users to make precise, localized edits to both content and motion within videos.
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The methodology involves a three-stage training strategy—motion prior training, decoupling training, and deblocking training—to refine content and motion control.
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ReVideo demonstrates superior performance compared to existing methods like InsV2V and Pika, with high PSNR scores and better text alignment.
Precise Content and Motion Control in Video Editing with ReVideo
Introduction to Video Editing with Diffusion Models
Video editing using AI has come a long way, especially with the advent of diffusion models, which offer significant improvements over traditional approaches. These models can transform text or images into high-quality videos and have opened doors to various personalization techniques, like adding control signals to guide the generation process. However, one area that has remained challenging is precise video editing, particularly when it involves both content and motion adjustments in specific areas of the video.
ReVideo is a novel approach that aims to tackle this exact problem. Unlike previous methods, which often focus solely on altering visual content or rely on coarse textual descriptions, ReVideo allows users to make precise, localized edits to both content and motion within videos.
Key Contributions of ReVideo
ReVideo introduces several innovations that make it stand out:
- Localized Editing of Content and Motion: For the first time, users can edit specific areas of a video by modifying the first frame for content and using trajectory lines for motion.
- Three-Stage Training Strategy: This approach addresses the imbalances and coupling issues between content and motion control, refining the model from coarse to fine adjustments.
- Spatiotemporal Adaptive Fusion Module (SAFM): This module integrates content and motion control effectively across different sampling steps and spatial locations.
The Core Methodology
Workflow Overview
- Content Editing: Users can modify the first frame of the video to set the desired content.
- Motion Control: Motion is controlled using trajectory lines, offering an intuitive way to specify the movement of objects within the video.
- Training Strategy: The model undergoes a three-stage training process:
- Motion Prior Training: Focuses on learning the sparse and abstract motion trajectories.
- Decoupling Training: Separates the learning of content and motion by using different videos for edited and unedited regions.
- Deblocking Training: Fine-tunes the key and value embeddings in temporal self-attention layers to eliminate boundary artifacts and maintain the motion control learned previously.
Experimentation and Results
ReVideo's performance was evaluated through extensive experiments, showcasing its flexibility and robustness across various video editing scenarios:
- Maintaining Content While Changing Motion: Demonstrates the ability to keep the visual content constant while applying new motion trajectories.
- Changing Content with Constant Motion: Allows users to modify the visual content in specific regions without altering the motion.
- Combining Edits: Users can simultaneously change both content and motion, even extending these edits to multiple areas within the same video.
Comparison with Other Methods
ReVideo was compared with other cutting-edge methods such as InsV2V, AnyV2V, and Pika. The results showed that ReVideo excels in maintaining the consistency of unedited content while allowing precise control over both content and motion in edited areas. Some strong numerical results from the comparison include:
- PSNR Scores: ReVideo achieved a PSNR of 32.85, closely matching Pika's 33.07, indicating high-quality reconstruction of unedited content.
- Text Alignment: ReVideo scored 0.2304, outperforming Pika's 0.2184, reflecting better alignment with the editing descriptions.
- Human Evaluation: 59.1% of participants preferred ReVideo overall, highlighting its superior performance in achieving precise editing targets.
Implications and Future Directions
ReVideo represents a significant step towards more refined and flexible video editing with AI. The ability to precisely control both content and motion in specific regions of a video could have numerous practical applications, from film and media production to personalized content creation for marketing and social media.
Future developments could explore enhancing the intuitive interaction experience, possibly integrating more sophisticated user interfaces or even real-time editing capabilities. Expanding the range of editable motions and improving the model's efficiency and scalability could further solidify ReVideo's position in the evolving landscape of AI-driven video editing.
Conclusion
ReVideo is a significant advancement in the field of video editing, offering precise and intuitive control over both content and motion. By addressing key challenges and introducing innovative solutions like the three-stage training strategy and the spatiotemporal adaptive fusion module, ReVideo sets a new benchmark for what is achievable with AI in video editing. The strong experimental results underscore its potential to revolutionize how we interact with and personalize video content.
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