Reference Image Filling for Recognizable Object Insertion
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Solution Overview
Problem
Existing image generation systems face challenges in accurately inserting objects from a reference image into an input image while maintaining the object's distinctiveness and recognizability, especially when guided by natural language text descriptions, and require additional training for image encoders.
Innovation Solution
An image editing apparatus that uses a reference image and a text prompt to generate a composite image by encoding the reference image and text prompt into embeddings, employing a self-attention operation to insert the reference image into the input image, thereby preserving the object's identity without needing additional training for the image encoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image generation systems are used to insert objects from a reference image into an input image, then the object insertion can be performed, but the object's distinctiveness and recognizability are not maintained
Solution Approach 1:
The patent introduces a reference image as an intermediary element that serves as a template for object insertion. The reference image contains the target object with desired characteristics, and the system uses it to guide the generation process, ensuring the inserted object maintains distinctiveness and recognizability while being properly integrated into the input image
2Manufacturing precision
If additional training is applied to image encoders to improve object insertion quality, then the insertion accuracy improves, but the computational overhead and training time increase
Solution Approach 1:
The patent performs preliminary encoding of the reference image into a latent representation before the actual object insertion process. This pre-processing step captures the essential features and characteristics of the target object, allowing the system to reuse this encoded information without requiring additional training during the insertion operation, thus improving computational efficiency
Solution Approach 2:
The system uses the reference image itself to guide the object insertion process without requiring external training data or additional model training. The reference image provides self-contained information about the desired object characteristics, allowing the encoder to learn and apply the necessary transformations autonomously during inference
Data Source
AI summary
Embodiments include systems and methods for generative image filling based on text and a reference image. In one aspect, the system obtains an input image, a reference image, and a text prompt. Then, the system encodes the reference image to obtain an image embedding and encodes the text prompt to obtain a text embedding. Subsequently, a composite image is generated based on the input image, the image embedding, and the text embedding.