Neural Volumetric Performance Capture for Arbitrary View Relighting
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Solution Overview
Problem
Existing image capture and rendering systems struggle to accurately render subjects from arbitrary viewpoints with scene-appropriate lighting, often requiring manual intervention and failing to capture full 3D shape, leading to unrealistic or inaccurate renderings.
Innovation Solution
A system utilizing a Light Stage with neural networks to extract features from multi-view imagery, pool them into a common texture space, and apply desired lighting conditions, enabling photorealistic renderings without manual correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional geometric pipelines are used to capture and render 3D subjects, then the rendering process is simpler and faster, but the accuracy of 3D shape capture and lighting realism deteriorates
Solution Approach 1:
The patent replaces traditional geometric mesh-based rendering systems with neural network-based volumetric rendering. Instead of using explicit geometric models and hand-crafted shading algorithms, the system uses trained neural networks to directly predict 3D shapes and synthesize photorealistic images from multi-view inputs, achieving superior accuracy in capturing complex geometries and lighting effects.
Solution Approach 2:
The system combines multiple data modalities (multi-view RGB images, depth maps, normal maps) and integrates them through neural network processing to create a composite volumetric representation. This composite approach fuses information from various sources to achieve more accurate and complete 3D reconstructions than any single input modality could provide alone.
2Reliability
If manual intervention is used to correct and modify captured images, then lighting and appearance can be adjusted, but the time and labor required increases significantly
Solution Approach 1:
The system performs preliminary action by pre-training neural networks on large datasets of images with various lighting conditions and 3D geometries. This pre-training enables the network to automatically understand and synthesize realistic lighting effects without requiring manual adjustment during actual image capture or processing, significantly reducing time while maintaining high lighting accuracy.
Solution Approach 2:
The neural rendering system is self-service in that the trained neural networks automatically perform lighting synthesis, shadow generation, and appearance correction without human intervention. The system self-adjusts lighting parameters and rendering settings based on the input images and desired output conditions, eliminating the need for manual image processing while maintaining professional-quality results.
3Adaptability or versatility
If existing image capture systems are used without specialized equipment, then the system is simpler and more accessible, but the ability to capture full 3D shape and enable arbitrary viewpoint rendering deteriorates
Solution Approach 1:
The patent achieves universality by creating a neural rendering system that can handle multiple functions: 3D shape reconstruction, arbitrary viewpoint synthesis, lighting relighting, and material property estimation all within a single integrated framework. The system can process various input types (RGB images, depth maps) and generate diverse outputs (novel views, relit images, 3D models), making it highly adaptable to different application scenarios.
Solution Approach 2:
The system transitions from 2D image space to 3D volumetric space by using neural networks to infer and represent 3D geometry and properties from 2D multi-view inputs. This dimensional transformation enables arbitrary viewpoint rendering by allowing the system to synthesize images from any camera position in 3D space, not just the original capture viewpoints.
Data Source
AI summary
Example embodiments relate to techniques for volumetric performance capture with neural rendering. A technique may involve initially obtaining images that depict a subject from multiple viewpoints and under various lighting conditions using a light stage and depth data corresponding to the subject using infrared cameras. A neural network may extract features of the subject from the images based on the depth data and map the features into a texture space (e.g., the UV texture space). A neural renderer can be used to generate an output image depicting the subject from a target view such that illumination of the subject in the output image aligns with the target view. The neural render may resample the features of the subject from the texture space to an image space to generate the output image.