HDR Panorama Lighting Prediction Using Synthetic Portrait Data
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Solution Overview
Problem
Existing lighting estimation deep learning models are biased towards certain demographic groups due to the high cost and time required for collecting paired portrait image and HDR environment map datasets, limiting their ability to cover diverse environments and subjects.
Innovation Solution
A deep learning model trained on a synthetic dataset of diverse subjects and environments, using a generator and discriminator architecture with specific loss functions, to predict HDR environment panoramas from LDR limited field-of-view portrait images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real paired portrait image and HDR environment map datasets are collected for training, then model training accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent uses synthetic portrait images generated from 3D face models as copies of real portraits, paired with corresponding HDR environment maps. This copying approach allows training without expensive real-world data collection, while maintaining the structural relationships needed for accurate lighting estimation.
Solution Approach 2:
The patent extracts only the essential lighting and geometric information needed for training by using simplified 3D face models and synthetic rendering, separating the critical training elements from the complexity of real-world data collection.
2Measurement precision
If real paired portrait image and HDR environment map datasets are collected for training, then model training accuracy is improved, but cost increases significantly
Solution Approach 1:
The patent creates synthetic training data by rendering 3D face models under controlled lighting conditions, copying the essential visual and lighting properties without the logistical burden of real-world data collection.
Solution Approach 2:
The synthetic data generation process is self-contained, using automated rendering pipelines that generate paired images and environment maps without requiring human subjects, equipment, or coordination.
3Productivity
If small real datasets are used for training, then training cost and time are reduced, but model bias towards certain demographic groups increases
Solution Approach 1:
The patent varies parameters of the 3D face models including ethnicity, age, gender, and facial features to generate diverse synthetic portraits. This parameter variation ensures broad demographic coverage while maintaining efficient automated training.
Solution Approach 2:
The synthetic data generation system serves multiple functions: it creates diverse demographic representations, ensures consistent lighting-ground truth pairings, and enables scalable dataset generation across different population groups.
Data Source
AI summary
Aspects of lighting estimation, and models therefor are provided including aspects to train such models. There is provided a lighting estimation model pre-trained using synthetic data to alleviate the costs and difficulty in obtaining real portrait image and HDR environment map paired datasets. To improve model performance, the model is training utilizing a discriminator configured to predict one or more average color values of a defined percentage of highest intensity pixels of a predicted environment map and to determine a color loss associated with the predicted environment map and the one or more average color values. The trained model can be used for a wide range of downstream tasks, including being used to generate hair renderings with realistic lighting effects for virtual try on experiences.