3D-Aware Diffusion Image Editing for Identity-Preserving Transformations
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Solution Overview
Problem
Existing methods for 3D object editing in images generated by diffusion models struggle to preserve the identity of objects during transformations such as translations, rotations, and changes in scene perspective, often resulting in loss of 3D object identity or requiring costly and complex training.
Innovation Solution
Implement 3D-aware transformations using a diffusion model by inverting the model to recover initial state information, applying 3D transformations to intermediate representations, and regenerating the image with preserved object identity through depth maps and text prompts, without the need for additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing methods are used for 3D object editing in diffusion models, then transformations can be applied, but object identity is lost
Solution Approach 1:
The patent lifts 2D image features into a 3D space using depth maps, creating 3D representations of the object. This dimensional transformation allows 3D transformations (rotation, translation, scaling) to be applied in the lifted space while preserving the object's identity, which cannot be achieved by operating solely in 2D image space.
Solution Approach 2:
The patent introduces an intermediary representation (3D features in lifted space) between the original 2D image and the final transformed image. This intermediary allows the transformation to occur in a space where geometric operations are more natural and preserve identity, then projects back to 2D for the final output.
2Manufacturing precision
If complex training is performed to preserve object identity, then transformation accuracy improves, but computational cost increases
Solution Approach 1:
The patent uses a pre-trained diffusion model as a base and creates a modified version that incorporates depth map processing and 3D transformation capabilities. Instead of completely retraining a new model, it adapts the existing model by adding depth awareness and 3D transformation operations, significantly reducing training costs while maintaining transformation accuracy.
Solution Approach 2:
The patent performs depth map generation and 3D feature lifting as preliminary steps before applying the actual transformation. By preparing the 3D representation in advance, the subsequent transformation operations can be applied more efficiently without requiring additional complex training.
3Device complexity
If 3D transformations are applied directly in 2D image space, then processing is simpler, but transformation realism decreases
Solution Approach 1:
The patent moves the transformation operations from 2D image space to a 3D lifted space using depth information. This allows geometric transformations to be applied in a space that better represents the actual 3D structure of the object, resulting in more realistic transformations when projected back to 2D, despite the increased processing complexity.
Data Source
AI summary
Techniques for implementing 3D-aware transformations using a diffusion model are disclosed. A computing system receives an image of a three-dimensional (“3D”) object, a text prompt, and a 3D transformation. The computing system generates an initial state for a diffusion model. The computing system generates, using the diffusion model, a second image of the 3D object and intermediate representations of the second image, based on the initial state, a depth map, and the text prompt. The computing system generates 3D representations of the second image based on the intermediate representations, transformed 3D representations of the second image by applying the 3D transformation, and edited intermediate representations. The computing system generates, using the diffusion model and the edited intermediate representations, an edited image of the 3D object, based on the initial state, a second depth map, and the text prompt and outputs the edited image.