DreamEdit: Subject-driven Image Editing (2306.12624v2)

Abstract: Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the previous works cannot precisely control the background and position of the target subject. In this work, we aspire to fill the void and propose two novel subject-driven sub-tasks, i.e., Subject Replacement and Subject Addition. The new tasks are challenging in multiple aspects: replacing a subject with a customized one can change its shape, texture, and color, while adding a target subject to a designated position in a provided scene necessitates a context-aware posture. To conquer these two novel tasks, we first manually curate a new dataset DreamEditBench containing 22 different types of subjects, and 440 source images with different difficulty levels. We plan to host DreamEditBench as a platform and hire trained evaluators for standard human evaluation. We also devise an innovative method DreamEditor to resolve these tasks by performing iterative generation, which enables a smooth adaptation to the customized subject. In this project, we conduct automatic and human evaluations to understand the performance of DreamEditor and baselines on DreamEditBench. For Subject Replacement, we found that the existing models are sensitive to the shape and color of the original subject. The model failure rate will dramatically increase when the source and target subjects are highly different. For Subject Addition, we found that the existing models cannot easily blend the customized subjects into the background smoothly, leading to noticeable artifacts in the generated image. We hope DreamEditBench can become a standard platform to enable future investigations toward building more controllable subject-driven image editing. Our project homepage is https://dreameditbenchteam.github.io/.

• The paper introduces DreamEdit, an iterative method that accurately replaces or adds subjects to images while preserving natural background context.
• It presents DreamEditBench, a curated dataset of 440 images across 22 subject types to evaluate subject-driven editing tasks.
• The study demonstrates that iterative refinement significantly enhances subject realism, validated by both automated metrics and human evaluations.

An Insightful Overview of "DreamEdit: Subject-driven Image Editing"

The paper "DreamEdit: Subject-driven Image Editing" introduces a novel approach to subject-driven image editing, addressing the challenges of integrating customized subjects into images while preserving background integrity and context. The authors focus on two primary tasks: Subject Replacement and Subject Addition, offering solutions to the inherent difficulties associated with these tasks.

Subject-driven image generation has gained significant traction; however, precise control over subject placement and background consistency remains an ongoing challenge. The authors introduce "DreamEdit," which fills this gap by emphasizing controllability and adaptability in subject-driven image editing.

Key Contributions

1. Task Definition:
   • Subject Replacement involves replacing a subject in a source image with a customized one, maintaining environmental coherence.
   • Subject Addition necessitates integrating a new subject into a specific position within a scene, ensuring natural interaction with the context.
2. Benchmark Development:
   • The authors present DreamEditBench, a curated dataset comprising 22 subject types across 440 source images. This dataset is designed to evaluate the efficacy of subject-driven editing tasks across varying difficulty levels.
3. Methodology:
   • The authors propose "DreamEditor," an innovative iterative generation method leveraging the existing text-to-image models, optimized for specific subject integration tasks. The method involves iterative refinement of the generated image to achieve a gradual adaptation, offering enhanced control over the final output.

Methodological Insights

The paper details the technical process of implementing "DreamEditor," emphasizing iterative in-painting techniques and the use of DDIM inversion for real image edition. By integrating tools like Segment-anything for segmentation and GLIGEN for initial placement in Subject Addition, the approach ensures that the generated subjects blend seamlessly into their environments.

• Iterative Generation: The strength of DreamEditor lies in its iterative approach, where the model progressively refines subject attributes and background context. This iterative process helps overcome initial model limitations by allowing gradual adjustment in subject realism and environmental harmony.

Evaluation

The paper presents comprehensive evaluations using both automated metrics and human evaluations to gauge subject consistency, background consistency, and realism. Notably, the results highlight the disparities between automated evaluations and human perceptions, underscoring the need for rigorous human assessments in evaluating image synthesis quality.

Implications and Future Directions

DreamEdit and the associated benchmark, DreamEditBench, provide a framework for advancing controllable image editing technologies. This work lays a foundation for further exploration into more sophisticated models capable of handling diverse and complex real-world scenarios. As such, DreamEditBench is poised to serve as a standardized platform for future research into controllable subject-driven image editing.

Conclusion

The paper presents a robust framework for enhancing the controllability in subject-driven image editing, bridging existing gaps between image generation and editing domains. DreamEditor demonstrates notable advancements in generating realistic and contextually appropriate subject integrations, contributing significantly to the field of computer vision and image manipulation. Continued exploration in this domain promises to yield even more sophisticated models, enhancing the practical applicability of image synthesis technologies across various industries.