VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing (2502.17258v1)

Abstract: Recent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level, instance-level, and part-level modifications, remains a formidable challenge. The major difficulties in multi-grained editing include semantic misalignment of text-to-region control and feature coupling within the diffusion model. To address these difficulties, we present VideoGrain, a zero-shot approach that modulates space-time (cross- and self-) attention mechanisms to achieve fine-grained control over video content. We enhance text-to-region control by amplifying each local prompt's attention to its corresponding spatial-disentangled region while minimizing interactions with irrelevant areas in cross-attention. Additionally, we improve feature separation by increasing intra-region awareness and reducing inter-region interference in self-attention. Extensive experiments demonstrate our method achieves state-of-the-art performance in real-world scenarios. Our code, data, and demos are available at https://knightyxp.github.io/VideoGrain_project_page/

An In-depth Exploration of VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing

The research paper introduces VideoGrain, a novel framework designed to address the challenges of multi-grained video editing. This task remains formidable due to the intricate requirements of modifying video content at various granularity levels, including class-level, instance-level, and part-level. The fundamental obstacles stem from semantic misalignment in text-to-region control and feature coupling inherent within diffusion models during video editing processes.

Core Challenges and Innovation

Multi-grained video editing extends beyond conventional video manipulation techniques, necessitating precise modifications across different granular levels. Class-level editing modifies objects within the same class, instance-level editing distinguishes each separate instance, and part-level editing involves fine-tuning specific attributes or adding new objects. Previous methodologies have struggled due to the indiscriminate coupling of features, which hinders the accurate distinction and modification of instances and classes within video frames.

To combat these challenges, VideoGrain innovates through the modulation of space-time attention mechanisms, specifically designed for zero-shot video editing. This approach amplifies attention for each local prompt to its corresponding spatially disentangled region, while minimizing extraneous interaction, thus enhancing text-to-region control and ensuring feature separation. The model achieves intra-region awareness and mitigates inter-region interference by selectively modulating both cross-attention and self-attention layers throughout space and time.

Methodological Framework

VideoGrain employs a Spatial-Temporal Layout-Guided Attention (ST-Layout Attn) mechanism, which is pivotal to ensuring that each textual prompt accurately transforms the designated video region. In the cross-attention layer, the model precisely enhances the attention on the pertinent spatial regions corresponding to each text prompt. Concurrently, the modulation of the self-attention layer aims to restrict feature coupling, thereby focusing attention and interaction within the specific region across frames, thus preserving the video’s temporal consistency and coherence.

The attention modulation is controlled dynamically, enhancing the network’s focus on intra-region features while minimizing inter-region interference. This control is orchestrated without necessitating parameter tuning, facilitating seamless integration into existing video editing processes.

Experimental Results and Comparatives

Extensive experiments conducted on real-world datasets demonstrate VideoGrain’s superior performance over previous state-of-the-art methods, such as FateZero, ControlVideo, Ground-A-Video, and DMT. Quantitative metrics such as CLIP-F, CLIP-T, Warp-Err, and Q-edit show marked improvement, underscoring the framework’s ability to maintain both qualitative fidelity and quantitative robustness in video edits. VideoGrain’s computational efficiency is further highlighted by its reduced memory footprint and faster edit times compared to existing methodologies.

Qualitative comparisons further affirm the model's proficiency in achieving nuanced edits, including complex instances where previous methods falter due to the inability to discriminate between similar class-level instances or manage intricate part-level modifications.

Implications and Future Prospects

VideoGrain sets a significant precedent for future advances in AI-driven video editing, illustrating the potential for achieving sophisticated multi-grained edits while maintaining visual integrity and temporal coherence. The framework opens numerous avenues for ongoing research and development, particularly in improving the granularity of control and extending these techniques to more complex scenarios involving dynamic and non-rigid motion across frames.

The model also suggests prospective integrations with emerging video foundation models, potentially enhancing adaptive video editing capabilities and expanding the scope and application of diffusion-based models in multimedia and AI research fields.