Multimodal 3D Geometry Generation With Diffusion Adapter Training
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Solution Overview
Problem
Existing methods for generating 3D geometries of deformable objects, such as faces, are time-consuming and resource-intensive, and traditional techniques like parametric shape models struggle to represent continuous deformations and can produce unrealistic shapes, while generative models face challenges in generating 3D shapes from textual or sketch-based inputs.
Innovation Solution
A machine learning model is trained using a diffusion model and adapter models to generate 3D geometries based on various conditioning inputs, including parametric shape models, sketches, images, and text, allowing for multimodal conditional generation of 3D shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manual 3D sculpting is used, then 3D geometries can be generated with high precision, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical sculpting operations with an automated machine learning system. The diffusion model generates 3D geometries automatically from conditioning inputs, eliminating the need for users to manually manipulate 3D models while maintaining high generation precision through learned representations.
Solution Approach 2:
The system creates synthetic training data by generating 3D geometries from 2D images and conditions through the diffusion model. This copying process enables the model to learn from synthesized examples rather than requiring direct manual sculpting, accelerating the generation process while preserving quality.
2Ease of operation
If parametric shape models are used, then the modeling process is simplified, but continuous nonlinear deformations cannot be represented and unrealistic shapes are produced
Solution Approach 1:
The patent transitions from linear parametric combinations to non-linear parameter transformations through the diffusion model. The model learns complex mappings between conditioning inputs and 3D geometries, enabling representation of continuous nonlinear deformations while maintaining ease of use through conditional generation interfaces.
Solution Approach 2:
The system combines multiple data modalities (2D images, sketches, text conditions) with 3D geometry representations through the diffusion model. This composite approach integrates diverse input types to generate accurate 3D shapes that capture complex deformations while remaining user-friendly through multi-modal conditioning.
3Productivity
If generative models are used to create 3D geometries from text prompts, then generation speed improves, but it is difficult to achieve desired visual and geometric characteristics
Solution Approach 1:
The diffusion model incorporates feedback mechanisms through iterative denoising processes and loss functions that compare generated geometries against target conditions. This feedback loop enables the model to adjust and refine outputs, achieving both fast generation and high precision in visual and geometric characteristics.
Solution Approach 2:
The system creates a universal diffusion model that handles multiple conditioning modalities (text, images, sketches, 3D conditions) within a single framework. This multi-functional approach maintains high generation speed while improving precision by leveraging the strengths of different input types through unified processing.
4Manufacturing precision
If multiple types of conditioning inputs are used, then the 3D geometry can reflect desired characteristics more accurately, but the system complexity increases
Solution Approach 1:
The diffusion model is designed as a universal system that accepts multiple conditioning modalities through a single unified architecture. This multi-functional design enables accurate generation from diverse inputs (text, images, sketches, 3D conditions) without proportionally increasing system complexity, as all modalities are processed through shared components.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for training a machine learning model on a geometry generation task. The technique includes generating, via execution of a diffusion model, a first set of training output corresponding to a first set of three-dimensional (3D) geometries based on a first set of conditioning inputs associated with a first conditioning mode, and training the diffusion model based on a first set of loss values associated with the first set of training output. The technique further includes generating, via execution of the diffusion model and a first adapter model, a second set of training output corresponding to a second set of 3D geometries based on a second set of conditioning inputs associated with a second conditioning mode, and training the first adapter model based on a second set of loss values associated with the second set of training output.