Neural Network Hair Rendering Temporal Stability
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Solution Overview
Problem
Enabling computing devices to perform image processing operations on digital images captured in varying conditions such as changes in scale, noise, lighting, movement, or geometric distortion is computationally intensive and challenging, especially for power and resource-constrained devices, which affects the efficiency and latency of image data processing in augmented reality applications.
Innovation Solution
The implementation of a neural network model that generates temporally consistent predictions for virtual hair in real-time, using conditioning on the previous frame and a loss function to produce smooth and stable hairstyle changes, reducing high-frequency texture jitter and improving rendering quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image processing operations are performed on digital images captured in varying conditions, then image processing quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a neural network-based system. The neural network model automatically learns and applies appropriate processing operations for different image conditions (scale changes, noise, lighting, movement, geometric distortion) without requiring explicit programming of each processing step, thereby reducing computational complexity while maintaining processing quality.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific conditions of each image. The neural network analyzes image characteristics and automatically modifies processing parameters to optimize quality for varying conditions such as noise levels, lighting variations, and geometric distortions, reducing the need for complex manual parameter tuning.
2Manufacturing precision
If real-time virtual hair generation is implemented, then rendering quality is improved, but power consumption and processing latency increase
Solution Approach 1:
The neural network model is pre-trained on extensive datasets of hair images and transformations before deployment. This preliminary training phase allows the model to learn complex hair rendering patterns in advance, enabling it to generate high-quality virtual hair in real-time with reduced computational power during actual operation, thereby lowering power consumption while maintaining rendering quality.
3Stability of the object's composition
If temporal consistency is achieved through previous frame conditioning, then rendering stability is improved, but processing latency increases
Solution Approach 1:
The system uses the previous frame's processed output as a conditional input for generating the current frame. By copying and leveraging information from the previously rendered frame, the neural network maintains temporal consistency and reduces high-frequency texture jitter without requiring complete re-processing of all image elements, thereby achieving rendering stability with minimal additional latency.
Data Source
AI summary
The subject technology trains a neural network based on a training process. The subject technology selects a frame from an input video, the selected frame comprising image data including a representation of a face and hair, the representation of the hair being masked. The subject technology determines a previous predicted frame. The subject technology concatenates the selected frame and the previous predicted frame to generate a concatenated frame, the concatenated frame being provided to the neural network. The subject technology generates, using the neural network, a set of outputs including an output tensor, warping field, and a soft mask. The subject technology performs, using a warping field, a warp of the selected frame and the output tensor. The subject technology generates a prediction corresponding to a corrected texture rendering of the selected frame.