17985579. TARGET-AUGMENTED MATERIAL MAPS simplified abstract (ADOBE INC.)
Contents
- 1 TARGET-AUGMENTED MATERIAL MAPS
TARGET-AUGMENTED MATERIAL MAPS
Organization Name
Inventor(s)
Valentin Deschaintre of London (GB)
Milos Hasan of San Jose CA (US)
TARGET-AUGMENTED MATERIAL MAPS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17985579 titled 'TARGET-AUGMENTED MATERIAL MAPS
Simplified Explanation
The patent application abstract describes a method for rendering images using target-augmented material maps, involving a graphics imaging application, a scene, an input material map, a target image file, and a pre-trained generative adversarial network (GAN).
- The graphics imaging application loads a scene, an input material map, and a target image file.
- The application accesses a stored material generation prior based on a GAN.
- The input material appearance is encoded to produce a projected latent vector.
- The value for the projected latent vector is optimized to generate a material map for rendering the scene with a realistic target material appearance.
Potential Applications
This technology could be applied in industries such as virtual reality, gaming, interior design, and fashion to create realistic and visually appealing images.
Problems Solved
This technology solves the problem of generating material maps with realistic target material appearances, enhancing the visual quality of rendered images.
Benefits
The benefits of this technology include improved image rendering quality, increased realism in virtual environments, and enhanced user experience in various applications.
Potential Commercial Applications
"Enhancing Image Rendering with Target-Augmented Material Maps" could find commercial applications in virtual reality content creation, video game development, architectural visualization, and e-commerce product visualization.
Possible Prior Art
Prior art may include methods for image rendering using material maps, GAN-based image generation techniques, and applications of machine learning in graphics rendering.
Unanswered Questions
How does this technology compare to traditional methods of image rendering?
This technology leverages pre-trained GANs to optimize material maps for rendering scenes with realistic target material appearances, potentially offering more efficient and visually appealing results compared to traditional rendering techniques.
What are the limitations or challenges of implementing this technology in real-world applications?
Some potential challenges of implementing this technology could include computational resource requirements for optimizing latent vectors, potential limitations in handling complex material appearances, and the need for specialized expertise in using GAN-based approaches for image rendering.
Original Abstract Submitted
Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.