Single-Selfie Diffusion Pipeline for Low-Power Face Enhancement
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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, and geometric distortion, is computationally intensive and challenging, particularly in resource-constrained environments.
Innovation Solution
A generative machine learning pipeline that processes a selfie image, a text condition, and a pose template to generate augmented reality content, utilizing neural networks and diffusion models to enhance and restore facial features, reducing latency and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing operations are performed on digital images captured in varying conditions, then image quality and processing accuracy are improved, but computational intensity and power consumption increase
Solution Approach 1:
The image processing pipeline is divided into multiple specialized components: face detection module, face alignment module, attribute recognition module, and image generation module. Each component handles a specific aspect of processing, allowing the system to process only relevant portions of images and reduce overall computational intensity while maintaining accuracy.
Solution Approach 2:
The system performs face detection, alignment, and attribute recognition before actual image generation or filtering. By pre-processing and identifying key features in advance, the system reduces the computational complexity of subsequent operations and enables faster processing with lower power consumption.
2Manufacturing precision
If complex image processing operations are performed, then image quality is improved, but processing time increases
Solution Approach 1:
The processing pipeline segments complex image operations into independent modules (detection, alignment, attribute recognition, generation). This allows parallel processing of different aspects and optimizes the execution sequence, reducing total processing time while maintaining high image quality through specialized processing at each stage.
Solution Approach 2:
Critical preprocessing steps such as face detection and alignment are performed before main image generation. This preliminary action prepares the data in advance, allowing the main processing to work with pre-processed inputs and significantly reducing overall processing time without compromising final image quality.
3Productivity
If resource-constrained devices perform image processing, then processing capability is improved, but device performance and battery life deteriorate
Solution Approach 1:
The processing system is segmented into essential core functions and optional enhancements. On resource-constrained devices, only critical processing steps are executed, while less essential operations can be omitted or simplified, maintaining adequate processing capability while preserving battery life.
Solution Approach 2:
The system implements selective processing where only necessary image regions and features are processed in full detail. For resource-constrained devices, processing can be partially applied or simplified without affecting the core functionality, thereby reducing energy consumption while maintaining acceptable processing capability.
Data Source
AI summary
The subject technology receives an input image, the input image comprising a selfie. The subject technology transforms, using a neural network, the input image to a latent representation of an identity. The subject technology transforms, using a diffusion model, a text condition to a second latent representation compatible with the latent representation of the identity. The subject technology transforms a pose template to a set of latent features for the diffusion model. The subject technology generates an intermediate image based on the latent representation of the identity, the second latent representation, and the set of latent features. The subject technology modifies, using a face enhancement network, the intermediate image based on the input image. The subject technology generates, using a face restoration network, a final output image based on the modified intermediate image. The subject technology provides for display the final output image on a display of a client device.