StableVideo: Text-driven Consistency-aware Diffusion Video Editing (2308.09592v1)

Abstract: Diffusion-based methods can generate realistic images and videos, but they struggle to edit existing objects in a video while preserving their appearance over time. This prevents diffusion models from being applied to natural video editing in practical scenarios. In this paper, we tackle this problem by introducing temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the edited objects. Specifically, we develop a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next. We then build up a text-driven video editing framework based on this mechanism, namely StableVideo, which can achieve consistency-aware video editing. Extensive experiments demonstrate the strong editing capability of our approach. Compared with state-of-the-art video editing methods, our approach shows superior qualitative and quantitative results. Our code is available at \href{https://github.com/rese1f/StableVideo}{this https URL}.

StableVideo: Text-driven Consistency-aware Diffusion Video Editing

The paper "StableVideo: Text-driven Consistency-aware Diffusion Video Editing" explores an innovative approach to enhancing the capabilities of existing diffusion models for natural video editing. While diffusion-based models traditionally excel in generating realistic imagery, they encounter challenges when editing existing video content due to the need for maintaining a consistent appearance over time. This paper presents a novel solution by integrating temporal dependencies into these models, thereby introducing the StableVideo framework that achieves consistency-aware video editing.

Methodological Contributions

The authors propose two key technological innovations within their framework. Firstly, they introduce an inter-frame propagation mechanism based on layered representations. This mechanism ensures that appearance information from one frame can be effectively propagated to subsequent frames, maintaining geometric consistency across time. This targeted approach allows for more stable and reliable video editing outputs, addressing a critical gap in existing methodologies where object appearance consistency was often neglected.

Secondly, the framework incorporates an aggregation network designed to generate edited atlases from key frames. This network is crucial for ensuring that edits applied to key frames are accurately reflected across the entire video, thus maintaining both temporal smoothness and fidelity to the original content's geometry.

Experimental Analysis

The experimental validation of StableVideo demonstrates its strong performance against state-of-the-art video editing methods. Through extensive qualitative and quantitative evaluations, the authors show that their approach not only produces high-quality edits but also achieves superior temporal consistency in comparison to existing solutions. The framework's efficacy is further highlighted by its reduced computational complexity, making it more practical for application in real-world scenarios.

Implications and Future Directions

StableVideo sets a new benchmark for video editing frameworks by effectively leveraging diffusion models for tasks they were not initially designed for. The implications extend beyond practical video editing, influencing fields such as media production, special effects, and virtual reality content creation, where maintaining temporal and geometric consistency is essential.

The research opens new possibilities for further enhancements. Future developments could explore the integration of more sophisticated temporal propagation mechanisms and the expansion of the framework to accommodate a wider range of content types, including non-rigid and highly dynamic scenes. Additionally, advancements in model training can further enhance the realism and richness of edited content, pushing the boundaries of what is currently achievable with diffusion-based frameworks.

In conclusion, the paper provides valuable insights and significant advancements in the domain of video editing, presenting a cohesive and robust solution to a complex problem faced by contemporary diffusion models. It sets the stage for future research that could expand upon these foundations to explore the full potential of text-driven video editing techniques in artificial intelligence.