Sketch-Guided 3D Object Generation Through 2D Denoising
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Solution Overview
Problem
Existing text-to-image generation processes are limited in customization and require artistic and 3D modeling expertise, failing to effectively utilize human-generated 3D sketches for creating 3D content.
Innovation Solution
A method that renders a 3D object from a defined camera position, adds noise to the image, and uses a pretrained 2D sketch-to-2D image model to denoise and update the 3D object representation based on a loss computed between denoised and original images, optimizing the 3D object from a 3D free-form sketch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text-to-image generation processes are used to create 3D content, then content generation is simplified, but customization options are limited and require non-original sample images
Solution Approach 1:
The system uses a pretrained 2D sketch-to-2D image model to generate a denoised 2D image from a noisy 2D image, effectively copying and refining the user's 3D sketch into a polished 2D representation that can then be used to guide 3D content generation
Solution Approach 2:
The patent introduces a 2D image representation as an intermediary between the 3D sketch and the final 3D content. The 2D image serves as a bridge that translates the user's sketch into a format that can be processed by the optimization system to generate customized 3D content
2Reliability
If traditional content creation processes are used for 3D content, then quality can be maintained, but artistic training and 3D modeling expertise are required
Solution Approach 1:
The system enables users to create 3D content directly from their sketches without requiring artistic training or 3D modeling expertise. The automated optimization process handles the complex transformations, allowing users to serve their own creative needs without professional skill requirements
Solution Approach 2:
The patent replaces manual artistic creation processes with an automated computational system. Instead of requiring users to manually model 3D objects, the system uses neural networks and optimization algorithms to automatically generate 3D content from 2D sketches, substituting mechanical manual work with automated digital processing
3Adaptability or versatility
If 3D free-form sketch is used as input, then customization is enhanced, but the process requires optimization from 2D to 3D representation
Solution Approach 1:
The system transforms the 3D sketch into a 2D image representation through rendering, then uses the 2D sketch-to-2D image model to process this 2D representation. This dimensional transformation simplifies the optimization process by working in 2D space before generating the final 3D output
Solution Approach 2:
The system performs preliminary rendering of the 3D sketch into a 2D image before optimization. This preliminary 2D representation is then processed by the pretrained model to generate a denoised 2D image, which serves as guidance for optimizing the final 3D content. The preliminary action breaks down the complex 3D optimization into manageable 2D processing steps
Data Source
AI summary
Text-to-image generation generally refers to the process of generating an image from one or more text prompts input by a user and in some cases also a user provided sample image. Existing text-to-image generation processes are configured to only generate content from text and usually non-original sample images (e.g. obtained from the Internet). This limits the customization options available to the user. The present disclosure provides a sketch-to-3D content generation process which allows users to generate 3D content from a given 3D human generated, or free-form, sketch, which enables greater customization of computer generated 3D content.