3D Face Reconstruction With Occlusion Segmentation Integration
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Solution Overview
Problem
In three-dimensional face reconstruction scenarios, the independent deployment of face reconstruction and occlusion segmentation models leads to excessive computing resource occupation due to increased computing workload, affecting the operation of other services on platforms with limited computing power.
Innovation Solution
A parameter prediction model integrating image feature extraction and segmentation decoding is trained until an association loss function reaches a predetermined state, allowing simultaneous three-dimensional face reconstruction and occlusion segmentation, reducing resource occupation and calculation amount.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If three-dimensional face reconstruction model and face occlusion segmentation model are independently deployed, then the functions of 3D face reconstruction and occlusion region segmentation are achieved, but excessive computing resources are occupied and the computing workload of the platform is increased
Solution Approach 1:
The patent merges the face reconstruction model and occlusion segmentation model into a single integrated system. The occlusion segmentation module processes occluded regions in the input image, while the face reconstruction model uses both the original image and segmentation results to generate the final 3D reconstruction. This integration reduces the number of independent models from two to one, decreasing deployment complexity and computing resource occupation while maintaining the reliability of both functions.
2Adaptability or versatility
If multiple independent models are deployed for face reconstruction and occlusion segmentation, then comprehensive face processing functions are achieved, but the computing workload of the platform is increased
Solution Approach 1:
The integrated system performs multiple functions through a unified architecture. The occlusion segmentation module identifies occluded regions, and the face reconstruction model simultaneously processes both occluded and non-occluded regions to generate comprehensive 3D face reconstruction. This multi-functional integration maintains adaptability for handling various face images (with or without occlusion) while improving productivity by reducing the computing workload associated with running multiple separate models.
3Measurement precision
If independent models are used for three-dimensional face reconstruction and occlusion segmentation, then accurate face reconstruction and precise occlusion positioning are achieved, but occupation of computing resources is excessive
Solution Approach 1:
The system segments the processing task into two coordinated modules: an occlusion segmentation module that identifies occluded regions with high precision, and a face reconstruction model that uses these segmentation results along with the original image to perform 3D reconstruction. This segmentation approach maintains measurement precision for occlusion positioning while reducing computing resource consumption by integrating both functions into a single deployed system rather than running multiple independent models.
Data Source
AI summary
Provided is a method for reconstructing a three-dimensional face based on occlusion segmentation. The method includes: inputting a target face image to a preconstructed parameter prediction model, wherein the parameter prediction model includes an image feature extractor and an image segmentation decoder, and the parameter prediction model is trained based on a plurality of face training images, face key point information from the plurality of face training images, and a face occlusion segmentation region, until an association loss function between the image feature extractor and the image segmentation decoder reaches a predetermined state; and outputting a target face reconstruction parameter and a target face occlusion region of the target face image based on the parameter prediction model, and performing three-dimensional face reconstruction post-processing based on the target face reconstruction parameter and the target face occlusion region.