Editable Free-Viewpoint Video via Layered Neural Representation
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Solution Overview
Problem
Conventional view synthesis methods are not suited for virtual reality (VR) and augmented reality (AR) applications, as they limit the resolution of reconstructed scenes, result in uncanny texture renderings, and lack editing capabilities, especially in dense scenes with motion.
Innovation Solution
A machine learning model is developed that encodes a scene into an environment layer and dynamic entity layers, using deformation and neural radiance modules to generate editable free-viewpoint videos by representing the scene as a continuous function of space and time, allowing for manipulation of objects and environment separately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If model-based solutions are used for view synthesis, then the reconstruction process is computationally efficient, but the resolution of reconstructed meshes is limited and texture renderings become uncanny
Solution Approach 1:
The scene is segmented into multiple independent layers including environment layer, dynamic entity layers, and foreground object layers. Each layer is represented by separate neural radiance fields that can be independently trained and manipulated, allowing high-resolution reconstruction without requiring a single complex monolithic model.
Solution Approach 2:
The patent transitions from traditional 2D image-based reconstruction to a 4D continuous representation (3D spatial coordinates plus time dimension). This dimensional expansion allows the model to capture temporal dynamics and generate high-resolution novel views from multiple angles and time points simultaneously.
2Adaptability or versatility
If model-based solutions focus on reconstruction of novel views, then the view synthesis capability is achieved, but editing and manipulation capabilities are lost
Solution Approach 1:
By segmenting the scene into environment and dynamic entity layers, the patent enables independent manipulation of different scene components. Users can edit, remove, or modify specific entities without affecting the overall scene reconstruction quality, as each layer maintains its own neural representation.
Solution Approach 2:
The patent introduces dynamic entity layers that can be independently transformed and manipulated in 3D space. These layers support real-time editing operations such as moving, resizing, or removing objects while maintaining consistent texture rendering through the neural radiance field representation.
3Manufacturing precision
If conventional methods are used for dense scenes with motion, then the processing speed is maintained, but occlusions cause uncanny texture renderings
Solution Approach 1:
The patent performs preliminary action by training separate neural radiance fields for environment and dynamic entities before rendering. This pre-training phase captures occlusion relationships and motion patterns, enabling high-quality texture rendering during actual rendering without real-time computational overhead.
Solution Approach 2:
By adding the time dimension to create a 4D continuous representation, the model can predict and handle occlusions that occur during camera movement or object motion. The temporal dimension allows the system to infer hidden surfaces and maintain consistent textures across different time points and viewing angles.
Data Source
AI summary
A computer-implemented method of generating editable free-viewport videos is provided. A plurality of video of a scene from a plurality of views is obtained. The scene comprises includes an environment and one or more dynamic entities. A 3D bounding-box is generated for each dynamic entity in the scene. A computer device encodes a machine learning model including an environment layer and a dynamic entity layer for each dynamic entity in the scene. The environment layer represents a continuous function of space and time of the environment. The dynamic entity layer represents a continuous function of space and time of the dynamic entity. The dynamic entity layer includes a deformation module and a neural radiance module. The deformation module is configured to deform a spatial coordinate in accordance with a timestamp and a trained deformation weight. The neural radiance module is configured to derive a density value and a color.