3D-Aware Image Generation with Continuous Attribute Control
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Solution Overview
Problem
Conventional text-to-image generation models struggle to accurately control 3-dimensional characteristics of elements in images due to limitations in describing these attributes through text and inefficiencies in training with precise object movements and camera parameters, leading to inefficient and computationally burdensome image generation processes.
Innovation Solution
A two-stage training process is employed for an image generation model, where the first stage learns the identity of an element from a text prompt and the second stage learns the continuous attribute input, using a continuous control model to generate a synthetic image with desired 3-dimensional characteristics, such as orientation and illumination direction, by combining text and attribute embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If text-to-image diffusion models are trained with precise object movements and camera parameters, then image generation accuracy is improved, but training efficiency and computational cost deteriorate
Solution Approach 1:
The patent segments the training process into two distinct stages: a first stage for learning object identity from text prompts, and a second stage for learning continuous attributes (orientation, illumination, camera parameters). This segmentation allows each stage to focus on specific aspects, improving training efficiency while maintaining generation accuracy.
Solution Approach 2:
The patent introduces a continuous control model that pre-processes attribute inputs and generates control embeddings before they are used in the main image generation process. This preliminary action separates the complex attribute control task from the main training loop, improving computational efficiency.
2Ease of operation
If text prompts are used to describe 3-dimensional characteristics, then ease of operation is improved, but control precision deteriorates
Solution Approach 1:
The patent introduces an intermediary representation layer - the continuous control model that receives text descriptions of 3D characteristics and converts them into precise control embeddings. This intermediary translates human-friendly text descriptions into mathematically precise control signals for the image generation model.
Solution Approach 2:
The patent changes the parameter representation from direct text descriptions to embedded vector representations in a continuous control model. This transforms discrete text inputs into continuous parameter spaces, enabling precise control while maintaining ease of operation through natural language inputs.
3Device complexity
If attributes are entangled with object identity in training, then training simplicity is improved, but image generation control deteriorates
Solution Approach 1:
The patent segments the training objectives into two independent parts: object identity learning in the first stage and attribute control learning in the second stage. This segmentation allows attributes to be disentangled from object identity, enabling independent control of each aspect during generation.
Solution Approach 2:
The continuous control model performs preliminary learning of attribute representations during training, creating a separate knowledge base for attributes that can be independently applied during generation. This preliminary action enables precise attribute control without entangling it with object identity.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a text prompt describing an element and an attribute value for a continuous attribute of the element, embedding the text prompt to obtain a text embedding in a text embedding space, embedding the attribute value to obtain an attribute embedding in the text embedding space, and generating a synthetic image based on the text embedding and the attribute embedding, where the synthetic image depicts the continuous attribute of the element based on the attribute value.