20230134690. NEURAL RENDERING FOR INVERSE GRAPHICS GENERATION simplified abstract (NVIDIA CORPORATION)
NEURAL RENDERING FOR INVERSE GRAPHICS GENERATION
Organization Name
Inventor(s)
Antonio Torralba Barriuso of Somerville MA (US)
NEURAL RENDERING FOR INVERSE GRAPHICS GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230134690 titled 'NEURAL RENDERING FOR INVERSE GRAPHICS GENERATION
Simplified Explanation
The abstract describes an approach for training an inverse graphics network, which can accurately reconstruct 3D objects from 2D images with minimal annotation of training data. The approach involves using an image synthesis network to generate training data for the inverse graphics network, which then teaches the synthesis network about the physical 3D controls. This allows for the extraction and disentanglement of 3D knowledge learned by generative models, enabling a controllable 3D "neural renderer" to complement traditional graphics renderers.
- An inverse graphics network is trained to reconstruct 3D objects from 2D images.
- An image synthesis network generates training data for the inverse graphics network.
- The inverse graphics network teaches the synthesis network about the physical 3D controls.
- The approach requires minimal annotation of training data.
- Differentiable renderers are used to extract and disentangle 3D knowledge learned by generative models.
- The disentangled generative model functions as a controllable 3D "neural renderer" alongside traditional graphics renderers.
Potential Applications
- Accurate 3D reconstruction of objects from 2D images.
- Generating realistic 3D models for virtual reality or augmented reality applications.
- Enhancing computer graphics rendering capabilities.
- Improving object recognition and understanding in computer vision systems.
Problems Solved
- Overcoming the need for extensive annotation of training data for 3D reconstruction.
- Enabling the extraction and disentanglement of 3D knowledge learned by generative models.
- Providing a controllable 3D "neural renderer" to complement traditional graphics renderers.
Benefits
- Accurate 3D reconstruction without the need for extensive manual annotation.
- Improved control and understanding of 3D models generated by generative models.
- Enhanced capabilities for computer graphics rendering and object recognition.
Original Abstract Submitted
approaches are presented for training an inverse graphics network. an image synthesis network can generate training data for an inverse graphics network. in turn, the inverse graphics network can teach the synthesis network about the physical three-dimensional (3d) controls. such an approach can provide for accurate 3d reconstruction of objects from 2d images using the trained inverse graphics network, while requiring little annotation of the provided training data. such an approach can extract and disentangle 3d knowledge learned by generative models by utilizing differentiable renderers, enabling a disentangled generative model to function as a controllable 3d “neural renderer,” complementing traditional graphics renderers.
