Pose-Preserved Text-to-Image Diffusion for 3D Domain Adaptation
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Solution Overview
Problem
Conventional 3D generative models struggle with maintaining camera pose consistency and style coherence when adapting across significant domain gaps, leading to low-quality and diverse 3D images.
Innovation Solution
A 3D image creation method using a server that performs domain adaptation by preserving the pose of source images through depth maps and converting their style according to text inputs, employing a pose-preserved diffusion model and a text-to-image diffusion technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If domain adaptation is applied across large domain gaps using conventional technologies, then the model can generate images in different domains, but the camera pose cannot be maintained and style consistency is degraded
Solution Approach 1:
The patent segments the domain adaptation process into two distinct components: pose preservation (using depth maps and viewpoint parameters) and style transformation (using text-to-image diffusion). This segmentation allows each component to be optimized independently, maintaining pose consistency while enabling style adaptation across domains.
Solution Approach 2:
The patent introduces depth maps as an intermediary element that mediates between the source image and the generated image. The depth map preserves the three-dimensional structure and pose information, acting as a bridge that maintains geometric consistency while allowing style transformation through the diffusion model.
2Adaptability or versatility
If conventional 3D generative models are trained with large collections of images from various fields, then the model can learn diverse domains, but the training process becomes complex and requires pre-labeling camera poses
Solution Approach 1:
The patent uses text descriptions as copies or representations of domain styles, avoiding the need to collect and pre-process actual images from target domains. The text-to-image diffusion model learns to generate domain-specific styles from text inputs, eliminating the need for extensive image collection and camera pose pre-labeling.
Solution Approach 2:
The patent changes the training parameters from requiring pre-labeled camera poses and extensive image collections to using text descriptions and depth maps. This parameter change simplifies the training process while maintaining the ability to generate diverse domain-specific 3D images.
3Ease of manufacture
If adaptation technologies are extended directly to 3D generative models, then training data can be collected from various domains, but the created images lack consistency of desired intention and viewpoint with the originals
Solution Approach 1:
The patent performs preliminary action by generating depth maps from source images before the style transformation process. This preliminary depth map generation preserves the three-dimensional structure and viewpoint information, ensuring that the subsequent style adaptation does not compromise pose consistency or viewpoint accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where the generated images are evaluated for pose consistency and style accuracy. The diffusion model uses this feedback to refine the generation process, ensuring that the output maintains both the desired viewpoint and the intended style from the text description.
Data Source
AI summary
A 3D image creation method that is performed by a server and able to adapt to domains having a large gap according to an embodiment includes: (a) collecting a plurality of training data including a set of a depth map about a source image in a first domain, a text indicative of a style of a second domain, and a target image of the second domain; (b) performing training to preserve a pose of the source image according to the depth map and converting the source image to be implemented in a style of the target image according to the text by using each of the training data; and (c) creating a plurality of 3D images corresponding to a specific domain from noise data randomly input by using a domain-adapted 3D generative model constructed based on the training and a predetermined pose parameter.