Pose Synthesis in Unseen Human Poses via Occlusion Handling
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Solution Overview
Problem
Current machine learning techniques struggle to generate new poses for individuals in input images with multiple people and complex backgrounds, particularly when there are occlusions, leading to poor pose estimation and image quality in real-world scenarios.
Innovation Solution
The proposed solution involves a system that includes a camera array, segmentation module, pose estimation module, and pose synthesizer, utilizing generative adversarial networks (GANs) and convolutional neural networks (CNNs) to separate backgrounds, detect occlusions, and apply geometric transformations to synthesize images of individuals in target poses, even in low-resolution and dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to generate new poses based on a single person in clean background contexts, then pose translation accuracy is improved, but the system fails to handle multiple people with occlusions and complex backgrounds
Solution Approach 1:
The system segments the input image into multiple person instances and processes each person separately through individual pose estimation pipelines. This segmentation allows the system to handle occlusions by focusing on visible body parts of each person independently, resolving the contradiction between maintaining pose accuracy and handling complex multi-person scenarios.
Solution Approach 2:
The patent introduces an intermediary occlusion detection and resolution module that mediates between the input image and pose estimation. This intermediary layer identifies occluded regions and applies appropriate handling strategies, enabling the system to maintain pose estimation accuracy even when people are occluded or in complex backgrounds.
2Adaptability or versatility
If the system processes images with multiple people and occlusions, then adaptability to real-world scenarios is improved, but pose estimation accuracy deteriorates due to occlusions and complexity
Solution Approach 1:
The system dynamically adjusts its processing strategy based on detected occlusion levels and scene complexity. When occlusions are detected, the system activates specialized handling routines that adapt the pose estimation process to the specific challenging conditions, maintaining accuracy across diverse real-world scenarios.
Solution Approach 2:
The patent changes key processing parameters dynamically based on scene analysis. When complex scenarios with occlusions are detected, the system modifies parameters such as confidence thresholds, processing depth, and model selection to optimize pose estimation accuracy for the specific conditions while maintaining broad adaptability.
3Device complexity
If traditional pose translation methods are used, then computational simplicity is maintained, but the system cannot handle occlusions and complex backgrounds effectively
Solution Approach 1:
The system performs preliminary actions by detecting occlusions and analyzing scene complexity before executing pose estimation. This preliminary analysis allows the system to prepare appropriate handling strategies in advance, ensuring reliable performance in occluded scenarios without requiring overly complex architecture during the main processing phase.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically detects challenging scenarios and adjusts its own processing parameters and model selection without external intervention. This self-adjusting capability improves reliability in occluded scenarios while maintaining relatively simple system architecture through automated adaptation.
Data Source
AI summary
Techniques related to synthesizing an image of a person in an unseen pose are discussed. Such techniques include detecting a body part occlusion for a body part in a representation of the person in a first image and, in response to the detected occlusion, projecting a representation of the body part from a second image having a different view into the first image. A geometric transformation based on a source pose of the person and a target pose is then applied to the merged image to generate a synthesized image comprising a representation of the person in the target pose.