Identity-Preserving Object Compositing for Diffusion Image Generation
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Solution Overview
Problem
Conventional image generation techniques using diffusion models fail to preserve the identity and geometry of objects during image compositing, resulting in poor visual consistency and harmonization.
Innovation Solution
A two-stage training process is employed, comprising a context-agnostic identity-preserving stage and an object compositing stage, where an image encoder learns view-invariant features in the first stage and a diffusion model blends the foreground object into the background in the second stage, ensuring identity preservation and geometric alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If diffusion models are used for image compositing, then image generation capability is improved, but identity preservation of objects deteriorates
Solution Approach 1:
The training process is segmented into two distinct stages: (1) identity-preserving representation learning stage where the encoder learns to preserve object identity across different views, and (2) compositing stage where the diffusion model performs the actual compositing. This segmentation allows each stage to optimize for its specific goal without interfering with the other, thereby maintaining both generation capability and identity preservation.
Solution Approach 2:
The encoder is pre-trained to learn identity-preserving representations before the compositing stage begins. This preliminary action ensures that the object identity is encoded in a view-invariant manner prior to the diffusion process, preventing identity loss during the generative compositing operation.
2Productivity
If conventional diffusion models perform compositing, then processing speed is improved, but visual consistency deteriorates
Solution Approach 1:
The identity-preserving encoder acts as an intermediary between the input foreground image and the diffusion model. It transforms the input into a view-invariant representation that maintains object identity while allowing the diffusion model to perform fast compositing operations. This intermediary ensures visual consistency is preserved even as the diffusion model operates at high speed.
3Device complexity
If single-stage training is used, then device complexity is reduced, but compositing quality deteriorates
Solution Approach 1:
The training process is divided into two specialized stages: identity-preserving representation learning and compositing training. Each stage has its own optimization objectives and data requirements, allowing the system to achieve high compositing quality by addressing identity preservation and compositing as separate, optimized sub-tasks rather than attempting to solve both simultaneously in a single-stage approach.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a foreground image and a background image. The foreground image depicts an object and the background image depicts a scene. The foreground image is encoded, using an image encoder of an image generation model, to obtain a foreground embedding. The foreground embedding preserves the identity of the object. A composite image is generated, using the image generation model, based on the background image and the foreground embedding. The composite image depicts the object from the foreground image within the scene from the background image.