Face Pose and Gaze Redirection Using Attribute-Based Image Generation
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Solution Overview
Problem
Existing image alteration techniques fail to convincingly redirect head poses and gazes of faces in images, particularly when faces are not directly facing the camera, and often rely on pixel-level modifications that lack accuracy and realism.
Innovation Solution
The use of machine-learning models, including multi-level attribute encoders and decoders, to normalize and rotate head poses and gazes, allowing for the generation of modified images with accurate and convincing redirection of faces to arbitrary positions without relying on pixel-level alterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pixel-level modifications are performed to redirect head pose and gaze, then the face can be altered to face the camera, but the realism and convincing accuracy of the modification deteriorates
Solution Approach 1:
The patent replaces traditional mechanical pixel-level warping methods with a machine learning-based approach. A neural network model is trained to predict head pose and gaze parameters, then generates modified images by transforming the input image based on these predictions rather than manually warping pixels. This substitution of mechanical image processing with intelligent prediction significantly improves the accuracy and realism of head pose and gaze redirection.
Solution Approach 2:
The patent changes the approach from direct pixel manipulation to parameter-based transformation. Instead of modifying individual pixels to achieve the effect, the system extracts and modifies high-level parameters (head pose angles, gaze direction) and uses these to generate the final image through learned transformations. This parameter-based approach maintains better structural integrity and realism compared to pixel-level alterations.
2Adaptability or versatility
If pixel-level warping is used to simulate face pointing at camera, then the face orientation can be changed, but the realism and natural appearance of the modified image deteriorates
Solution Approach 1:
The patent replaces mechanical pixel warping with a machine learning model that learns the complex transformations required for realistic head pose and gaze redirection. The neural network is trained on diverse data to understand natural variations in head orientation and eye direction, enabling it to generate realistic modified images that maintain natural appearance while achieving the desired face orientation.
Solution Approach 2:
The patent performs preliminary training of the machine learning model on extensive datasets containing various head poses, gazes, and facial expressions. This preliminary learning phase enables the model to understand the complex relationships between head orientation, eye direction, and facial geometry, so that when the model generates modified images, it can do so with high realism without requiring real-time complex computations.
3Manufacturing precision
If machine-learning models are used to normalize and rotate head poses and gazes, then the accuracy and realism of redirected faces improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent segments the image processing task into distinct components: head pose detection, gaze direction prediction, and image transformation. The machine learning model is divided into specialized modules that handle each aspect separately, allowing for more efficient processing while maintaining high accuracy. This segmentation reduces the overall computational complexity compared to a single monolithic model attempting to handle all transformations simultaneously.
Solution Approach 2:
The patent implements a two-stage approach where the model first predicts head pose parameters and then separately handles gaze redirection. This partial action strategy breaks down the complex transformation into manageable steps, reducing the computational burden at each stage while achieving the desired level of accuracy through the combination of multiple simpler transformations rather than a single complex operation.
Data Source
AI summary
Systems and techniques are described herein for generating an image. For instance, a method for generating an image is provided. The method may include obtaining a source image of a face having source attributes and exhibiting a source pose and source gaze; obtaining at least one of a target pose and a target gaze; and generating a modified image of the face having the source attributes and exhibiting at least one of the target pose and the target gaze.