Neural Volumetric Capture With Texture-Space 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 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 re-project them to desired viewpoints with scene-appropriate lighting, enabling photorealistic renderings without manual correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional geometric pipelines are used to capture and render 3D subjects, then the rendering process is computationally simpler and faster, but the rendering realism and lighting accuracy deteriorate
Solution Approach 1:
The patent replaces traditional geometric rendering pipelines with neural network-based rendering. The neural renderer learns complex lighting effects and material properties from training data, substituting explicit geometric modeling and radiance transport calculations with learned representations that achieve photorealistic results more efficiently.
Solution Approach 2:
The system changes the parameter representation from explicit geometric and lighting parameters to latent features learned by neural networks. The neural network extracts and represents complex lighting effects, material properties, and geometric information in a compressed feature space, enabling realistic rendering without manually specifying each parameter.
2Manufacturing precision
If manual intervention is used to correct captured images, then image quality can be improved, but the processing time and labor requirements increase
Solution Approach 1:
The system enables self-service image correction through neural networks that automatically extract features, separate lighting effects from material properties, and generate corrected renderings without human intervention. The neural renderer autonomously handles the complex task of relighting and view synthesis that previously required manual image editing.
Solution Approach 2:
Manual image correction processes are replaced with automated neural network-based image processing. The system uses learned representations to automatically correct lighting artifacts, remove shadows, and synthesize realistic renderings from captured images, eliminating the need for manual retouching.
3Adaptability or versatility
If arbitrary viewpoint rendering is attempted with traditional methods, then view flexibility increases, but rendering accuracy and lighting consistency deteriorate
Solution Approach 1:
The system transitions from 2D image capture to 4D representation by incorporating time as a dimension alongside spatial coordinates. The neural network processes spatiotemporal data to learn dynamic lighting effects and material properties that vary with viewpoint and time, enabling accurate rendering from arbitrary viewpoints while maintaining lighting consistency.
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.