Core Concepts
FlexEdit enables flexible and controllable object-centric image editing through optimization and blending mechanisms.
Abstract
The FlexEdit framework introduces a novel approach to object-centric image editing, addressing limitations in previous methods. It combines optimization with object constraints and latent blending using adaptive masks. The framework is evaluated across various editing scenarios and benchmarks, showcasing a balance between editing semantics and background preservation. A human preference study confirms the superiority of FlexEdit in generating edited images.
Controllable Object Replacement: Utilizes attention-based estimation methods for size and position control.
Object Addition: Addresses attention overlapping with a separation constraint.
Object Removal: Demonstrates effective object removal and inpainting.
Iterative Latent Manipulation: Shows the iterative process of latent optimization and blending.
Experimental Results: FlexEdit outperforms existing methods in achieving a balance between editing semantics and background preservation.
Ablation Studies: Demonstrates the robustness of FlexEdit to inversion methods, the importance of adaptive masks, and the impact of loss constraints.
Conclusions: FlexEdit presents a novel approach to object-centric image editing, showcasing its effectiveness and potential for future improvements.
Stats
"Our framework could achieve robust and flexible control over several text-guided object-centric editing scenarios."
"We demonstrate the versatility of FlexEdit in various object editing tasks and curate an evaluation test suite."
"FlexEdit integrates advanced components for flexible and precise object editing across diverse scenarios."
Quotes
"Our contributions are threefold: We propose a new editing framework for object-centric image editing tasks."
"We provide an extensive evaluation on different benchmarks and various state-of-the-art methods to showcase the versatility of our editing framework."