Multimodal 3D Model Generation With Text and 2D Priors
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Solution Overview
Problem
Conventional generative models struggle to generate diverse and high-resolution 3D objects due to limited training datasets, lacking the ability to effectively utilize text inputs and 2D priors for enhanced diversity and realism.
Innovation Solution
A scalable 3D generative model utilizing a Variational Auto-Encoder (VAE) trained on large datasets, incorporating text encoders and 2D/3D encoders, with Score Distillation Sampling (SDS) loss to improve generalization and diversity, and capable of generating material and physical properties of 3D objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional generative models are trained on limited 3D object datasets, then training time and computational resources are reduced, but the diversity and quality of generated 3D objects deteriorate
Solution Approach 1:
The patent introduces 2D images as an intermediary medium to bridge the gap between limited 3D data and diverse generation needs. By training on abundant 2D image data and using it as a mediator to guide 3D generation through SDS loss, the system achieves high diversity without requiring extensive 3D training datasets.
Solution Approach 2:
The patent transitions from traditional 3D-only training to a multi-dimensional approach by incorporating 2D image data and text descriptions. This dimensionality expansion allows the model to leverage diverse 2D priors and semantic information to generate varied 3D objects despite limited 3D training examples.
2Device complexity
If conventional generative models use basic training approaches, then model complexity is reduced, but the realism and resolution of generated 3D objects deteriorate
Solution Approach 1:
The patent combines multiple data types (2D images, text descriptions) and multiple training objectives (reconstruction loss, SDS loss) to create a composite training framework. This composite approach integrates diverse information sources to achieve high-resolution and realistic 3D generation without requiring a single overly complex model architecture.
Solution Approach 2:
The patent performs preliminary encoding of text descriptions and 2D images into latent representations before the main generation process. This preliminary action prepares structured priors that guide the 3D generation, improving realism and resolution without adding complexity to the core generative model.
3Device complexity
If conventional models cannot effectively utilize text inputs, then model simplicity is maintained, but text-to-shape generation quality and composability deteriorate
Solution Approach 1:
The patent implements a universal text encoding mechanism that serves multiple functions: guiding 3D shape generation, enabling compositional editing, and providing semantic priors for the SDS loss. This multi-functional text processing approach achieves high text-to-shape quality without requiring separate specialized modules for each function.
Data Source
AI summary
In various examples, systems and methods are disclosed relating to generating an output 3D latent representation by encoding, using a text encoder, a text prompt and encoding, using a 2D/3D encoder, a 2D image of an object or a 3D representation of the object. A 3D output is generated by applying the output 3D latent representation to a decoder. A reconstruction loss and a SDS loss are determined for the 3D output. At least one of the text encoder, the 2D/3D encoder, and the decoder is updated using the reconstruction loss and the SDS loss.