Text-to-Image Diffusion Models for Generalizable 3D Mesh Generation
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Solution Overview
Problem
Efficient generation of high-quality 3D meshes remains a challenging task due to data scarcity, with conventional approaches struggling to train generalizable models.
Innovation Solution
Leveraging large and pretrained 2D diffusion models, adapted to generate 3D meshes by fine-tuning them to predict multiple orthographic projections, which are then converted into triplane latent tensors and processed to create 3D meshes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used for 3D mesh generation, then training data requirements are high, but model generalization capability is poor
Solution Approach 1:
The patent introduces an intermediary process that converts 2D image data into 3D mesh representations through a series of transformations (2D diffusion model → multiview latent tensor → triplane latent tensor → 3D mesh). This intermediary approach allows leveraging abundant 2D training data to improve 3D mesh generation generalization without requiring large amounts of 3D training data.
Solution Approach 2:
The patent uses 2D images as copies or projections of 3D objects to train the model. By processing multiple orthographic projections (front, back, left, right, top, bottom views) of 2D images, the system learns to reconstruct 3D mesh structures from these 2D representations, effectively using 2D data as a substitute for 3D training data.
2Productivity
If 3D mesh generation models are trained with limited data, then training speed is fast, but generation quality is low
Solution Approach 1:
The patent transforms the problem from direct 3D mesh generation to a multi-dimensional approach: processing 2D images through multiple orthographic projections, converting to multiview latent tensors, then to triplane latent tensors, and finally to 3D meshes. This dimensional transformation allows leveraging 2D data efficiency while achieving 3D generation quality.
Solution Approach 2:
The patent segments the 3D mesh generation process into distinct stages: (1) generating multiview latent tensors from 2D images through orthographic projections, (2) converting to triplane latent tensors, and (3) decoding to final 3D meshes. This segmentation allows each stage to be optimized independently, maintaining training speed while improving generation quality.
3Productivity
If existing 3D generation methods are used, then model complexity is manageable, but generation speed is slow
Solution Approach 1:
The patent creates a universal framework that can process any 2D image input through the same pipeline (orthographic projection → multiview latent tensor → triplane latent tensor → 3D mesh). This multi-functional approach enables rapid generation across different object types while maintaining a consistent model architecture, improving generation speed without proportionally increasing complexity.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a multiview latent tensor is generated based on processing a textual input using a diffusion machine learning model, where the multiview latent tensor corresponds to a plurality of orthographic projections corresponding to the textual input. A triplane latent tensor is generated based on the multiview latent tensor using a conversion machine learning model, and a three-dimensional mesh is generated based on processing the triplane latent tensor using a decoder machine learning model.