3D Object Generation via Joint 2D and 3D Training
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Solution Overview
Problem
Existing AI-based methods for generating 3D content from 2D images face challenges in quality due to limited generalization and poor geometry, particularly when dealing with out-of-distribution test images.
Innovation Solution
A method involving joint 2D and 3D training of a machine learning model using datasets with single-view images labeled with texture information and multi-view images labeled with geometry information, enabling the generation of 3D content from a single 2D image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-view supervision is used to construct learnable 3D priors, then 3D information can be generated from 2D images, but the model suffers from poor generalization when test images are out-of-distribution due to limited 3D training data
Solution Approach 1:
The patent combines 2D and 3D training datasets into a unified joint training framework. The model simultaneously processes both 2D images (with texture labels) and 3D images (with geometry labels) during training, allowing the limited 3D data to be augmented and enhanced by the larger 2D dataset while still learning accurate 3D priors from the 3D portion.
Solution Approach 2:
The trained model achieves multi-functionality by being capable of generating 3D content from 2D inputs (the core function) while also maintaining robust generalization to out-of-distribution images. The joint training approach makes the model universal across both 2D and 3D data distributions, enabling it to handle diverse input scenarios effectively.
2Ease of manufacture
If 2D priors only are used to construct 3D content, then text-to-3D synthesis can be performed, but the generated content suffers from poor geometry due to ignorance of 3D information
Solution Approach 1:
The patent merges 2D prior knowledge (from pre-trained 2D text-to-image diffusion models) with 3D geometric information (from labeled 3D datasets) in a joint training framework. This combination allows the model to benefit from the simplicity of 2D priors while correcting their geometric deficiencies through supervised learning on 3D data.
Solution Approach 2:
The model undergoes parameter updates during joint training that transition it from relying solely on 2D priors to incorporating 3D geometric constraints. The training process modifies the model parameters to balance 2D texture fidelity with 3D geometric accuracy, achieving improved geometry while maintaining the ease of 2D-based synthesis.
3Reliability
If per-instance optimization is performed for each 3D generation task, then the model can adapt to specific inputs, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The patent performs preliminary joint training on comprehensive 2D and 3D datasets before deployment. This pre-training establishes robust 3D priors and geometric understanding in the model, eliminating the need for extensive per-instance optimization. The model is prepared in advance to handle specific inputs effectively without requiring time-consuming fine-tuning for each new generation task.
Data Source
AI summary
Virtual reality and augmented reality bring increasing demand for 3D content creation. In an effort to automate the generation of 3D content, artificial intelligence-based processes have been developed. However, these processes are limited in terms of the quality of their output because they typically involve a model trained on limited 3D data thereby resulting in a model that does not generalize well to unseen objects, or a model trained on 2D data thereby resulting in a model that suffers from poor geometry due to ignorance of 3D information. The present disclosure jointly uses both 2D and 3D data to train a machine learning model to be able to generate 3D content from a single 2D image.