LoRA-Edit: Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning (2506.10082v3)

Abstract: Video editing using diffusion models has achieved remarkable results in generating high-quality edits for videos. However, current methods often rely on large-scale pretraining, limiting flexibility for specific edits. First-frame-guided editing provides control over the first frame, but lacks flexibility over subsequent frames. To address this, we propose a mask-based LoRA (Low-Rank Adaptation) tuning method that adapts pretrained Image-to-Video (I2V) models for flexible video editing. Our approach preserves background regions while enabling controllable edits propagation. This solution offers efficient and adaptable video editing without altering the model architecture. To better steer this process, we incorporate additional references, such as alternate viewpoints or representative scene states, which serve as visual anchors for how content should unfold. We address the control challenge using a mask-driven LoRA tuning strategy that adapts a pre-trained image-to-video model to the editing context. The model must learn from two distinct sources: the input video provides spatial structure and motion cues, while reference images offer appearance guidance. A spatial mask enables region-specific learning by dynamically modulating what the model attends to, ensuring that each area draws from the appropriate source. Experimental results show our method achieves superior video editing performance compared to state-of-the-art methods. Project Page: https://cjeen.github.io/LoraEditPaper

• The paper introduces LoRA-Edit, a novel method using mask-aware LoRA fine-tuning to enable controllable and precise video editing on pre-trained Image-to-Video models.
• This mask-aware approach leverages spatial conditioning and reference images for fine-grained control over editable regions, ensuring background preservation and temporal consistency.
• Experimental results demonstrate that LoRA-Edit significantly outperforms state-of-the-art techniques on quantitative metrics like CLIP Score and DeQA Score, showing improved semantic alignment and quality.

Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning

The paper entitled "LoRA-Edit: Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning" presents a novel method for video editing that focuses on flexibility and control while utilizing diffusion models. The authors address existing limitations in large-scale pretraining approaches for video editing, which generally lack adaptability for specific edits, particularly when controlling frames beyond the initial one.

Methodology and Key Contributions

The proposed technique leverages Low-Rank Adaptation (LoRA) with a mask-driven strategy to fine-tune pretrained Image-to-Video (I2V) models, thus facilitating precise video editing. The approach employs a mask that delineates editable regions, enabling fine control over video edits while preserving background components. The LoRA framework is modified to dynamically attend different regions of a video based on masks, thereby maintaining coherent video editing that respects both spatial structures and appearance inputs from reference images.

The paper delineates the process of adapting the I2V model using mask-based LoRA as follows:

1. Disentangling Edits and Background: Utilizing spatial conditioning inputs allows differentiating between regions that should remain static and those requiring generation, thus ensuring the preservation of background content.
2. Leveraging Reference Images: By incorporating alternate viewpoints or representative scene states, the method provides anchors for content transition across frames. This helps refine the appearance of edited frames, ensuring adherence to user specifications throughout video sequences.
3. Efficient Model Training: Despite extensive model capabilities, the methodology ensures efficiency by allowing edit propagation without major alterations to the model architecture.

Experimental Results

The experiments show that this mask-aware tuning method significantly outperforms prior state-of-the-art techniques in video editing on quantitative metrics such as CLIP Score and DeQA Score, indicating superior semantic alignment and image quality. Moreover, qualitative assessments confirm the capacity of the presented framework to execute precise, temporally consistent edits while avoiding undesired changes in unedited regions.

Implications

This research has potentially significant practical applications across various domains needing granular video editing capabilities, offering a reliable pathway for flexible and efficient edits without model architecture modifications. Theoretically, the use of mask-aware tuning presents a promising direction for nuanced control over generative models, possibly leading to advancements in fine-tuning strategies beyond video editing, applying to other fields requiring customized generative model outputs.

Speculation on Future Developments

Looking forward, further refinement of LoRA fine-tuning methods could enhance resource efficiency, further broadening accessibility and flexibility in video editing tasks. Additionally, extending this mask-aware strategy to embed richer contextual understanding could improve generalization capabilities, potentially allowing for a wider range of complex edits across diverse datasets and applications.

In conclusion, by tackling the intricate challenge of controllable video editing through innovative application of LoRA and masking strategies, this paper contributes an important advancement in the field of AI-driven video content creation, setting new benchmarks for adaptability and user-controlled content generation.