Conditional 3D Shape Generation for Accurate Deformable Geometry
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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 to generate 3D geometries using a diffusion model and adapter models that incorporate various conditioning inputs, including parametric shape models, sketches, images, and text, to produce accurate and efficient 3D geometries.
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 that generates 3D geometries from text prompts. The system uses a diffusion model combined with adapter models to automatically create accurate 3D shapes without requiring manual intervention, thereby eliminating the time-consuming nature of traditional sculpting while maintaining geometric precision
Solution Approach 2:
The patent creates digital copies of 3D geometries through automated generation rather than manual creation. The machine learning model learns from existing 3D data and generates new geometries by copying and transforming patterns from training data, enabling rapid reproduction of accurate 3D shapes without the time investment required for manual sculpting
2Ease of operation
If parametric shape models are used, then the modeling process is simplified, but the models cannot represent continuous nonlinear deformations and produce unrealistic shapes
Solution Approach 1:
The patent changes the mathematical parameters used to represent 3D shapes from linear combinations in parametric models to a diffusion-based probabilistic framework. The diffusion model processes shapes through multiple stages of noise addition and removal, capturing continuous nonlinear deformations by transforming the parameter space in a way that preserves geometric accuracy while maintaining ease of operation through text-based input
Solution Approach 2:
The patent combines multiple model components into a composite system: a base diffusion model trained on general 3D geometry plus specialized adapter models that handle different conditioning inputs (text, sketches, images). This composite architecture integrates the simplicity of parametric models with the flexibility of generative models, achieving both ease of operation and high shape accuracy
3Loss of time
If generative models are used to create 3D geometries from text prompts, then generation time is reduced, but the models struggle to achieve desired visual and geometric characteristics
Solution Approach 1:
The patent segments the generative model into a base diffusion model and multiple adapter models, each specialized for different conditioning inputs such as text prompts, sketches, or images. This segmentation allows the system to leverage the time-saving benefits of generative modeling while improving accuracy by applying domain-specific adapter layers that refine the output based on the specific input modality and desired characteristics
Solution Approach 2:
The patent incorporates feedback mechanisms through the adapter models that adjust the diffusion process based on the conditioning inputs. The system uses guidance parameters and attention mechanisms to continuously refine the generated geometry in response to the input conditions, ensuring that the desired visual and geometric characteristics are achieved while maintaining rapid generation times
Data Source
AI summary
One embodiment of the present invention sets forth a technique for generating a geometry for a shape. The technique includes inputting, into a machine learning model, (i) a noise sample and (ii) one or more conditioning inputs. The technique also includes generating, via execution of the machine learning model based on the noise sample and the one or more conditioning inputs, a two-dimensional (2D) position map associated with the shape. The technique further includes generating a three-dimensional (3D) geometry for the shape based on the 2D position map.