Meta platforms technologies, llc (20240104828). Animatable Neural Radiance Fields from Monocular RGB-D Inputs simplified abstract
Contents
- 1 Animatable Neural Radiance Fields from Monocular RGB-D Inputs
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Animatable Neural Radiance Fields from Monocular RGB-D Inputs - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Animatable Neural Radiance Fields from Monocular RGB-D Inputs
Organization Name
meta platforms technologies, llc
Inventor(s)
Tiantian Wang of Merced CA (US)
Nikolaos Sarafianos of Sausalito CA (US)
Tony Tung of San Francisco CA (US)
Animatable Neural Radiance Fields from Monocular RGB-D Inputs - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104828 titled 'Animatable Neural Radiance Fields from Monocular RGB-D Inputs
Simplified Explanation
The abstract describes a patent application for a computing system that utilizes depth information to generate a point cloud of an image frame, and then generates latent representations based on the point cloud, tracking of temporal relationships, and camera parameters for free-viewpoint rendering of a dynamic scene.
- The computing system accesses image frames and depth information of a dynamic scene to generate a point cloud.
- It generates latent representations based on the point cloud, tracking of temporal relationships, and camera parameters.
- The system trains a neural radiance fields (NeRF) based model for free-viewpoint rendering of the dynamic scene using the latent representations.
Potential Applications
The technology described in the patent application could be applied in various fields such as virtual reality, augmented reality, gaming, and entertainment industries for creating realistic and immersive experiences.
Problems Solved
This technology solves the problem of generating realistic free-viewpoint renderings of dynamic scenes by utilizing depth information, tracking temporal relationships, and camera parameters to create accurate representations.
Benefits
The benefits of this technology include enhanced visual quality, improved realism in free-viewpoint rendering, and the ability to generate novel viewpoints of dynamic scenes with greater accuracy.
Potential Commercial Applications
The potential commercial applications of this technology include virtual reality content creation, video game development, movie production, and other entertainment industries where realistic rendering of dynamic scenes is crucial.
Possible Prior Art
One possible prior art for this technology could be the use of neural networks for image processing and rendering in computer graphics applications. Another could be the use of depth information for generating 3D representations of scenes in virtual environments.
Unanswered Questions
How does the system handle occlusions in the dynamic scene when generating free-viewpoint renderings?
The abstract does not provide information on how the system deals with occlusions in the scene to ensure accurate rendering from novel viewpoints.
What computational resources are required to implement this technology effectively?
The abstract does not mention the computational requirements or hardware specifications needed to run the system efficiently for real-time applications.
Original Abstract Submitted
in particular embodiments, a computing system may access a particular image frame and corresponding depth information of a dynamic scene. the depth information is used to generate a point cloud of the particular image frame. the system may generate a first latent representation based on the point cloud. the system may access a sequence of image frames of the dynamic scene and a set of key frames. the system may generate, using a temporal transformer, a second latent representation based on tracking and combining temporal relationship between the sequence of image frames and the set of key frames. the system may access camera parameters for rendering the one or more objects from a desired novel viewpoint and generate a third latent representation. the system may train an improved neural radiance fields (nerf) based model for free-viewpoint rendering of the dynamic scene based on the first, second, and third latent representations.
