Radiance Field Cage for Dynamic 3D Scene Rendering
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Solution Overview
Problem
Generating synthetic images of dynamic scenes in a controllable and photorealistic manner is challenging due to the complexity of creating rigged 3D models and the computational burden, especially in real-time applications like video calls and video games, where precise control over animation and generalization to new scenes are required.
Innovation Solution
A computer-implemented method using radiance fields and volume rendering, where a deformation description comprising a cage of primitive 3D elements and animation data from a physics engine or articulated object model is used to compute images, allowing for precise control over animation and reducing the burden of enrollment with fewer training images, enabling real-time operation and good generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a rigged 3D model is used to compute synthetic images of dynamic scenes, then the images can be generated with controlled animation, but the process becomes complex and time-consuming due to manual rigging work
Solution Approach 1:
The patent replaces the traditional mechanical rigging process (manual constraint setup and animation binding) with a machine learning-based radiance field system. The system learns scene representation and animation control directly from image data, eliminating the need for manual rigging while maintaining controllable animation capabilities
Solution Approach 2:
The patent creates a learned radiance field representation that copies and generalizes from training images to new scenes. This learned representation serves as a substitute for traditional rigged models, enabling animation control without requiring manual creation of rigid constraints and skinning weights
2Reliability
If traditional rendering methods are used for dynamic scenes, then photorealistic images can be achieved, but real-time computation is too slow
Solution Approach 1:
The patent performs preliminary computation during an offline training phase where a radiance field is learned from training images. This pre-computed representation enables fast real-time rendering by replacing complex traditional rendering calculations with efficient field queries and composition operations
Solution Approach 2:
The patent changes the computational parameters from traditional per-pixel ray tracing to a learned radiance field representation. This parameter transformation allows the system to achieve photorealistic results through field-based computations that are significantly faster than traditional rendering methods
Data Source
AI summary
To compute an image of a dynamic 3D scene comprising a 3D object, a description of a deformation of the 3D object is received, the description comprising a cage of primitive 3D elements and associated animation data from a physics engine or an articulated object model. For a pixel of the image the method computes a ray from a virtual camera through the pixel into the cage animated according to the animation data and computes a plurality of samples on the ray. Each sample is a 3D position and view direction in one of the 3D elements. The method computes a transformation of the samples into a canonical cage. For each transformed sample, the method queries a learnt radiance field parameterization of the 3D scene to obtain a color value and an opacity value. A volume rendering method is applied to the color and opacity values producing a pixel value of the image.