Snap inc. (20240296645). NEURAL RENDERING USING TRAINED ALBEDO TEXTURES simplified abstract
Contents
NEURAL RENDERING USING TRAINED ALBEDO TEXTURES
Organization Name
Inventor(s)
Vladislav Shakhrai of London (GB)
Sergey Demyanov of Santa Monica CA (US)
Mikhail Vasilkovskii of Playa Vista CA (US)
Aleksei Stoliar of Marina del Rey CA (US)
NEURAL RENDERING USING TRAINED ALBEDO TEXTURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296645 titled 'NEURAL RENDERING USING TRAINED ALBEDO TEXTURES
The patent application describes methods and systems for generating photorealistic renderings of objects using a combination of albedo textures and machine learning models trained on multiple viewpoints of the object.
- Accessing a set of albedo textures and a machine learning model trained on a real-world object.
- Obtaining a 3D mesh of the object.
- Receiving input for a new viewpoint different from the trained viewpoints.
- Generating a photorealistic rendering based on the new viewpoint, the 3D mesh, albedo textures, and the machine learning model.
Potential Applications: - Virtual reality and augmented reality applications - Product design and visualization - Gaming and entertainment industry
Problems Solved: - Enhancing realism in computer-generated imagery - Streamlining the rendering process for complex objects
Benefits: - Improved visual quality in virtual environments - Faster rendering times - Enhanced user experience in various applications
Commercial Applications: Title: "Advanced Photorealistic Rendering Technology for Virtual Environments" This technology can be utilized in industries such as architecture, interior design, e-commerce, and virtual tours to create realistic visualizations of products and environments.
Questions about the technology: 1. How does this technology improve upon traditional rendering methods? - This technology combines albedo textures and machine learning models to generate photorealistic renderings from new viewpoints, enhancing realism and efficiency. 2. What are the potential limitations of using machine learning models for rendering objects? - Machine learning models may require extensive training data and computational resources, but they can significantly improve rendering quality and speed.
Original Abstract Submitted
methods and systems are disclosed for performing operations for generating a photorealistic rendering of an object. the operations include: accessing a set of albedo textures and a machine learning model associated with a real-world object, the set of albedo textures and a machine learning model having been trained based on a plurality of viewpoints of the real-world object; obtaining a three-dimensional (3d) mesh of the real-world object; receiving input that selects a new viewpoint that differs from the plurality of viewpoints of the real-world object; and generating a photorealistic rendering of the real-world object from the new viewpoint based on the 3d mesh of the real-world object, the set of albedo textures, and the machine learning model associated with the real-world object.
