VIA: Unified Spatiotemporal Video Adaptation Framework for Global and Local Video Editing (2406.12831v3)

Abstract: Video editing serves as a fundamental pillar of digital media, spanning applications in entertainment, education, and professional communication. However, previous methods often overlook the necessity of comprehensively understanding both global and local contexts, leading to inaccurate and inconsistent edits in the spatiotemporal dimension, especially for long videos. In this paper, we introduce VIA, a unified spatiotemporal Video Adaptation framework for global and local video editing, pushing the limits of consistently editing minute-long videos. First, to ensure local consistency within individual frames, we designed test-time editing adaptation to adapt a pre-trained image editing model for improving consistency between potential editing directions and the text instruction, and adapts masked latent variables for precise local control. Furthermore, to maintain global consistency over the video sequence, we introduce spatiotemporal adaptation that recursively gather consistent attention variables in key frames and strategically applies them across the whole sequence to realize the editing effects. Extensive experiments demonstrate that, compared to baseline methods, our VIA approach produces edits that are more faithful to the source videos, more coherent in the spatiotemporal context, and more precise in local control. More importantly, we show that VIA can achieve consistent long video editing in minutes, unlocking the potential for advanced video editing tasks over long video sequences.

• The paper presents a unified framework for spatiotemporal video editing that ensures both local and global consistency using test-time adaptation and cross-frame attention.
• It adapts pre-trained image models to video by leveraging automated mask generation and an in-domain tuning process to enhance local detail and temporal coherence.
• Experimental evaluations show superior performance against state-of-the-art methods in instruction following, consistency, and overall edit quality over long video sequences.

Via: A Spatiotemporal Video Adaptation Framework for Global and Local Video Editing

The paper "Via: A Spatiotemporal Video Adaptation Framework for Global and Local Video Editing" introduces a sophisticated framework designed to address the inherent challenges associated with video editing. This research, authored by an interdisciplinary team from the University of California, Snap Research, KAUST, and the University of Texas at Dallas, offers significant advancements in maintaining both local and global consistency in video edits, specifically focusing on longer video sequences.

Video editing presents a spectrum of challenges, primarily concerning the preservation of the video's original integrity, executing user instructions with high accuracy, and maintaining consistent editing quality across spatial and temporal dimensions. Most existing methods typically falter in these areas, especially when dealing with longer video sequences. Via proposes a unified spatiotemporal video adaptation framework to achieve minute-long video editing with high consistency.

Methodology

Test-Time Editing Adaptation for Local Consistency

Via employs a novel test-time editing adaptation approach to ensure local consistency within video frames. This method adapts a pre-trained image editing model for video editing. The process involves using an in-domain tuning set obtained through an augmentation pipeline, which enhances the semantic understanding and consistency of editing directions relative to textual instructions. Additionally, the authors introduce local latent adaptation equipped with an automated mask generation tool that leverages multimodal LLMs and segmentation models. This aids in achieving precise local control of edits and preserving non-target areas across frames, ensuring detailed, consistent edits.

Spatiotemporal Adaptation for Global Consistency

To maintain global consistency throughout a video sequence, the framework employs a spatiotemporal adaptation mechanism. This involves a gather-and-swap strategy for cross-frame attention, which uses consistent attention variables during the model's architecture phase and applies them across the entire sequence. This method reinforces the temporal coherence of edits, ensuring that changes apply uniformly over long sequences, addressing one of the core limitations of previous methods which were only capable of handling shorter durations.

Experimental Results

Extensive experiments demonstrate Via's superiority in producing faithful and coherent edits compared to baseline methods. The framework significantly enhances the quality of both global edits—such as stylistic transformations—and local edits—such as object replacements or background changes. The results show that Via can edit minute-long videos with high precision and consistency, outperforming existing frameworks which are generally restricted to much shorter durations.

Human evaluations further validate Via’s achievements, showing that it surpasses state-of-the-art baselines such as Rerender, TokenFlow, AnyV2V, Video-P2P, and Tune-A-Video in three key criteria: Instruction Following, Consistency, and Overall Quality. The results consistently favor Via, underscoring its robustness in handling complex editing instructions while maintaining visual coherence over extended video sequences.

Implications and Future Work

The implications of this research are twofold: practical and theoretical. Practically, Via enables more advanced video editing tasks, opening up new possibilities for digital content creation in filmmaking, advertising, education, and social media. Theoretically, it sets a new standard in the domain of video editing by addressing and solving the dual challenges of local and global edit consistency over longer durations.

Future developments could focus on expanding the range of editing tasks by overcoming the constraints of the underlying image editing models. Additionally, automating the selection process for the root pair in the self-tuning method could further enhance the usability and efficiency of the framework.

In conclusion, Via represents a significant step forward in the domain of video editing, providing a robust solution to the previously unresolved issue of maintaining both local and global consistency across long video sequences. This framework stands to make a substantial impact on both academic research and practical applications in digital media.