Multi-view Neural Object Modeling for Photorealistic Head Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for generating digital images of heads struggle to create photorealistic images that include both skin and non-skin regions, and they often require manual tuning of parameters, which is time-consuming. Additionally, conventional machine learning models can only generate single images from nearly frontal views, leading to inconsistencies when viewing from different angles.
Innovation Solution
A computer-implemented method using a machine learning model that includes a neural radiance field model and an identity model. The method involves tracing a ray through a pixel into a virtual scene, sampling positions along the ray, and applying the machine learning model to determine density, diffuse color, and specular color for each position. The model is trained on images from multiple viewpoints, allowing it to generate photorealistic images of heads from various angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models are used to generate images of heads from multiple viewpoints, then the model can handle various viewing angles, but the head identity and appearance become inconsistent across viewpoints
Solution Approach 1:
The model segments the head representation into separate identity and viewpoint components. The identity encoder extracts viewpoint-invariant identity features, while the rendering network separately processes viewpoint information. This segmentation allows the model to maintain consistent head identity across different viewpoints by keeping identity features separate from viewpoint-dependent rendering features.
Solution Approach 2:
The identity encoder acts as an intermediary that extracts and preserves viewpoint-invariant identity information from input images. This intermediary component ensures that the core identity features remain consistent across different viewpoints by filtering out viewpoint-specific variations before passing features to the rendering network.
2Productivity
If conventional facial capture techniques are used, then skin regions can be captured, but non-skin regions cannot be captured and require time-consuming manual inpainting
Solution Approach 1:
The model enables self-service inpainting by automatically generating photorealistic non-skin regions (hair, eyes, eyebrows) without requiring manual intervention. The rendering network synthesizes these regions directly from the captured skin regions and identity features, allowing the system to complete the full head image autonomously.
Solution Approach 2:
The patent replaces manual mechanical inpainting operations with an automated neural network-based synthesis system. Instead of manually creating non-skin regions, the model uses learned patterns from training data to automatically generate realistic hair, eyes, and other facial features that conform to the captured identity.
3Manufacturing precision
If conventional machine learning models are trained on large datasets, then they can generate photorealistic images, but they require relatively large data sets with mostly frontal heads
Solution Approach 1:
The model extracts and leverages viewpoint-invariant identity information from training data, separating it from viewpoint-specific appearance variations. By focusing training on identity preservation rather than requiring examples of every possible viewpoint, the model achieves photorealism with fewer and more diverse training images.
Solution Approach 2:
The patent adds an identity dimension to the training approach by explicitly encoding viewpoint-invariant identity features. This dimensional transformation allows the model to generalize to unseen viewpoints without requiring exhaustive training data coverage of all possible viewing angles, reducing the overall training data requirements.
Data Source
AI summary
Techniques are disclosed for generating photorealistic images of objects, such as heads, from multiple viewpoints. In some embodiments, a morphable radiance field (MoRF) model that generates images of heads includes an identity model that maps an identifier (ID) code associated with a head into two codes: a deformation ID code encoding a geometric deformation from a canonical head geometry, and a canonical ID code encoding a canonical appearance within a shape-normalized space. The MoRF model also includes a deformation field model that maps a world space position to a shape-normalized space position based on the deformation ID code. Further, the MoRF model includes a canonical neural radiance field (NeRF) model that includes a density multi-layer perceptron (MLP) branch, a diffuse MLP branch, and a specular MLP branch that output densities, diffuse colors, and specular colors, respectively. The MoRF model can be used to render images of heads from various viewpoints.