Cross-Frame Attention for Seamless Image Edits and Detail Consistency
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Solution Overview
Problem
Current generative models struggle with maintaining consistency in scene lighting, reflections, and other semantic details during complex image edits, limiting users to simple replacements rather than direct adjustments, and requiring extensive manual adjustments for plausible edits.
Innovation Solution
An image generation model trained with a detail extraction model and a synthesizer model using cross-frame attention to regenerate coarsely edited images, incorporating relevant visual information from the unedited image, ensuring seamless integration of edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current generative models are used for complex image edits, then image generation capability is provided, but consistency in scene lighting, reflections, and semantic details deteriorates
Solution Approach 1:
The system segments the image editing task into two distinct models: a coarse editing model that handles large-scale structural changes and a detail refinement model that restores fine-grained details. This segmentation allows each model to specialize in specific aspects of image editing, maintaining both versatility and precision
Solution Approach 2:
The coarse editing model performs preliminary edits on the image first, making major structural changes. Then the detail refinement model takes this coarsely edited image and restores fine details. This preliminary action approach allows complex edits to be performed while maintaining detail consistency
2Ease of operation
If users perform direct adjustments on images, then editing flexibility is improved, but extensive manual adjustments are required to achieve plausible results
Solution Approach 1:
The detail refinement model automatically performs the restoration of fine details without user intervention. After the user performs coarse editing, the system self-service completes the refinement process by automatically restoring lighting consistency, reflection details, and semantic attributes, eliminating the need for extensive manual adjustments
3Manufacturing precision
If simple replacements are used instead of direct adjustments, then maintaining detail consistency is easier, but editing versatility is limited
Solution Approach 1:
The system provides dynamic editing capabilities where users can perform direct adjustments with full flexibility, and the system dynamically adapts by using the detail refinement model to maintain consistency. This dynamic approach allows both versatile editing and detail consistency to coexist
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for generating a seamless version of a coarse edit image includes obtaining a reference image, the coarse edit image, and an occlusion mask. The coarse edit image depicts an object from the reference image at a target position, and the occlusion mask indicates an occluded region of the coarse edit image. Embodiments then extract, using a detail extraction model of an image generation model, detail features from the reference image based on the occlusion mask. Subsequently, embodiments generate, using the image generation model, a synthetic image depicting the object at the target position based on the coarse edit image by performing cross-frame attention between the detail features and the coarse edit image.