Lemon Inc. (20240265628). GEOMETRY-AWARE THREE-DIMENSIONAL SYNTHESIS IN ALL ANGLES simplified abstract
Contents
GEOMETRY-AWARE THREE-DIMENSIONAL SYNTHESIS IN ALL ANGLES
Organization Name
Inventor(s)
Hongyi Xu of Los Angeles CA (US)
Sizhe An of Los Angeles CA (US)
Yichun Shi of Los Angeles CA (US)
Guoxian Song of Los Angeles CA (US)
Linjie Luo of Los Angeles CA (US)
GEOMETRY-AWARE THREE-DIMENSIONAL SYNTHESIS IN ALL ANGLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240265628 titled 'GEOMETRY-AWARE THREE-DIMENSIONAL SYNTHESIS IN ALL ANGLES
Simplified Explanation:
This patent application describes a three-dimensional generative adversarial network that includes a generator, a discriminator, and a renderer. The generator creates two-dimensional backgrounds for images and generates multi-grid representation features based on a latent code and camera pose. The renderer synthesizes images based on camera pose, camera pose offset, and multi-grid representation features, while the discriminator supervises the training of foreground masks.
- The generator creates backgrounds and multi-grid representation features based on a latent code and camera pose.
- The renderer synthesizes images using camera pose, camera pose offset, and multi-grid representation features.
- The discriminator supervises the training of foreground masks with up-sampled and super-resolved images.
Key Features and Innovation:
- Three-dimensional generative adversarial network
- Generator for creating backgrounds and multi-grid representation features
- Renderer for synthesizing images based on various parameters
- Discriminator for supervising training of foreground masks
Potential Applications:
- Image generation for virtual reality environments
- Video game development
- Augmented reality applications
- Computer-generated imagery in movies and TV shows
Problems Solved:
- Efficient generation of realistic images
- Seamless integration of foreground and background elements
- Enhanced training of image rendering models
Benefits:
- Improved visual quality in virtual environments
- Faster image generation process
- Enhanced realism in computer-generated imagery
Commercial Applications:
Virtual reality content creation tools for developers and designers
Questions about Three-Dimensional Generative Adversarial Network:
1. How does the generator create backgrounds for images? 2. What role does the discriminator play in the training process?
Original Abstract Submitted
a three-dimensional generative adversarial network includes a generator, a discriminator, and a renderer. the generator is configured to receive an intermediate latent code mapped from a latent code and a camera pose, generate two-dimensional backgrounds for a set of images, and generate, based on the intermediate latent code, multi-grid representation features. the renderer is configured to synthesize images based on the camera pose, a camera pose offset, and the multi-grid representation features; the camera pose offset being mapped from the latent code and the camera pose; and render a foreground mask. the discriminator is configured to supervise a training of the foreground mask with an up-sampled image and a super-resolved image.
