Zero-shot Image Editing with Reference Imitation (2406.07547v1)

Abstract: Image editing serves as a practical yet challenging task considering the diverse demands from users, where one of the hardest parts is to precisely describe how the edited image should look like. In this work, we present a new form of editing, termed imitative editing, to help users exercise their creativity more conveniently. Concretely, to edit an image region of interest, users are free to directly draw inspiration from some in-the-wild references (e.g., some relative pictures come across online), without having to cope with the fit between the reference and the source. Such a design requires the system to automatically figure out what to expect from the reference to perform the editing. For this purpose, we propose a generative training framework, dubbed MimicBrush, which randomly selects two frames from a video clip, masks some regions of one frame, and learns to recover the masked regions using the information from the other frame. That way, our model, developed from a diffusion prior, is able to capture the semantic correspondence between separate images in a self-supervised manner. We experimentally show the effectiveness of our method under various test cases as well as its superiority over existing alternatives. We also construct a benchmark to facilitate further research.

• The paper presents imitative editing using dual diffusion U-Nets to automatically apply reference content to masked regions in source images.
• It leverages a self-supervised training pipeline with video frames to discover semantic correspondences, achieving superior SSIM, PSNR, and lower LPIPS scores.
• The approach offers practical benefits for product design, character creation, and special effects by enabling intuitive, precise image modifications.

Zero-shot Image Editing with Reference Imitation

The paper "Zero-shot Image Editing with Reference Imitation" introduces a novel technique for image editing that addresses the challenge of precisely describing expected outcomes when modifying images. The proposed method, termed imitative editing, allows users to specify areas in the source image for editing and reference another image to guide how those areas should look after the edits. This approach leverages an automated system to find and use the relevant parts of the reference image, thereby facilitating a more intuitive editing process. The pipeline, referred to in the paper, demonstrates efficacy through experimental evaluations and establishes a new benchmark for the task.

Overview and Contributions

The primary contribution of this work is the introduction of imitative editing, a new approach to image editing that operates without the need for the user's meticulous instructions on how the reference image fits with the source image. To realize this, the authors utilize a generative training framework involving dual diffusion U-Nets. The training is self-supervised, where two frames from a video clip are used—one as the source image with masked regions and the other as the reference image. The key innovation lies in the self-supervised learning to discover semantic correspondences and adapt the reference image content to the masked source areas.

Methodological Contributions:

1. Imitative Editing: Simplifies user interaction by automatically finding and applying content from the reference image to the specified regions in the source image without requiring detailed fitment instructions.
2. Dual Diffusion U-Net Architecture: Utilizes two U-Nets—a reference U-Net and an imitative U-Net—with shared attention mechanisms to integrate features from both the source and reference images.
3. Training Pipeline: Implements a self-supervised approach by using video frames to naturally capture semantic correspondences and variations, aiding the model to generalize well across different scenarios.

Numerical Results and Benchmarks

The authors present quantitative results demonstrating the superiority of their approach over existing methods. The constructed benchmark evaluates:

• Part Composition: Ability to locate and replicate local parts from the reference image into the source image.
• Texture Transfer: Focus on transferring patterns or textures while preserving the structural integrity of the source objects.

In terms of performance metrics, the proposed method exhibits higher Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and achieves lower Learned Perceptual Image Patch Similarity (LPIPS) scores compared to alternative methods. Additionally, the evaluations based on image embeddings (using DINO and CLIP models) and text descriptions further reinforce the robustness of the approach.

Implications and Future Directions

The proposed framework addresses significant shortcomings in existing image editing models, particularly in scenarios demanding precise edits that are challenging to describe textually. By automating the fitment process and allowing reference-based edits, the approach has significant implications for practical applications such as:

• Product Design: Facilitates the visualization of modifications by applying desired features or patterns from one product onto another.
• Character Creation: Enables detailed enhancements by transferring specific features from reference images to character illustrations.
• Special Effects: Simplifies the process of adding intricate visual effects to images by leveraging content from other sources.

Theoretical implications suggest that the proposed dual U-Net architecture and self-supervised training using video frames can be expanded to other generative tasks. This method’s reliance on discovering and leveraging semantic correspondences can inspire future research in areas such as video-to-video translation, multimodal learning, and more sophisticated content composition models.

Future developments could focus on enhancing the model's ability to handle more complex scenarios, such as editing regions with highly intricate details or accommodating more challenging lighting variations. Additionally, integrating more sophisticated prompt-based guidance could further improve the usability and flexibility of imitative editing in various domains.

Conclusion

This paper presents a significant advancement in the field of image editing by introducing an intuitive, reference-based editing approach that automates the correspondence and adaptation of visual content. The dual diffusion U-Net framework and the self-supervised training pipeline illustrate a novel way to utilize video frames for robust model training. The findings and the proposed evaluation benchmark chart a promising direction for future research and applications in the domain of generative image editing and manipulation.