Neural Image Compositing with Layout Transformers for Precise Object Placement
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Solution Overview
Problem
Conventional image generation networks struggle with accurately inserting objects into images at appropriate positions, scales, and harmonizing them with the existing content, often requiring substantial manual edits and are not scalable to large numbers of classes.
Innovation Solution
A neural network using a sequential encoder to determine object classification, coordinates, and dimensions sequentially, enabling precise placement and scaling of new objects within images, and employing a bi-directional transformer model for layout understanding and object insertion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional image generation networks are used for object insertion, then the process can be automated, but the insertion accuracy (position, scale, harmony) deteriorates and manual edits are required
Solution Approach 1:
The patent segments the object insertion task into multiple sequential prediction steps: class prediction, x-coordinate prediction, y-coordinate prediction, width prediction, and height prediction. Each step is handled by a dedicated prediction head in the transformer model, allowing precise control over each aspect of object placement rather than attempting to solve all parameters simultaneously.
Solution Approach 2:
The patent introduces mask tokens as intermediary elements that represent the object to be inserted. These mask tokens are embedded in the token sequence and processed by the transformer model, which learns to predict their positions and dimensions. This intermediary representation allows the model to reason about object placement in a structured way, improving insertion accuracy.
2Device complexity
If conventional image generation networks are used for object insertion, then the system structure remains simple, but the quality of generated images deteriorates and substantial manual edits are required
Solution Approach 1:
The patent merges the object insertion task with the existing image representation by integrating mask tokens into the token sequence that the transformer model processes. This unified approach allows the model to simultaneously understand the existing image content and reason about the placement of new objects, improving generation quality without requiring separate complex systems.
Solution Approach 2:
The patent employs a dynamic sequential prediction process where the model predicts object attributes in a specific sequence (class, x, y, width, height) rather than all at once. This dynamic approach allows each prediction to build on previous predictions and the context from the image, improving overall insertion accuracy while maintaining a relatively simple transformer-based architecture.
3Adaptability or versatility
If conventional image generation networks are used, then the current system can be maintained, but scalability to large numbers of classes and diverse data domains deteriorates
Solution Approach 1:
The patent creates a universal object insertion system based on the transformer model that can handle diverse data domains and large numbers of classes. By representing objects as tokens and using a unified sequential prediction framework, the system can be applied to different types of images and object categories without requiring domain-specific modifications, achieving scalability through a single versatile architecture.
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure receive an image depicting an object; generate a sequence of tokens including a set of tokens corresponding to the object and a set of mask tokens corresponding to an additional object to be inserted into the image; generate a placement token value for the set of mask tokens based on the sequence of tokens using a sequence encoder, wherein the placement token value represents position information of the additional object; and insert the additional object into the image based on the position information to obtain a composite image.