Image Pose Editing Using User-Object Interaction Scoring
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Solution Overview
Problem
Conventional pose modification techniques require reference poses that may not be suitable for all scenarios, lack consideration of user surroundings, and have limited editing options, resulting in inaccurate and irrelevant pose generation.
Innovation Solution
A system and method that extracts user and object features, determines possible interactions, generates joint-object scores, and optimizes user poses based on containment scores and context, allowing for automatic pose modification in images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional pose modification techniques use reference poses from skilled models or social media platforms, then pose transfer can be achieved, but the reference poses are not suitable for all use case scenarios and result in inaccurate and irrelevant pose generation
Solution Approach 1:
The system enables automatic reference pose generation by analyzing the input image context, user characteristics, and object properties without requiring external reference poses from skilled models or social media platforms. The AI model autonomously generates poses tailored to each specific scenario
Solution Approach 2:
The system dynamically adjusts pose parameters based on extracted features including user body type, clothing characteristics, object properties, and environmental context. This allows the generated poses to adapt to different use case scenarios while maintaining anatomical correctness and contextual relevance
2Ease of operation
If conventional pose modification techniques require reference poses as input, then pose transfer is possible, but editing options and flexibility are limited
Solution Approach 1:
The system extracts and removes the requirement for external reference poses as input. Instead, it extracts relevant features directly from the input image (user characteristics, object properties, environmental context) and uses these to generate poses autonomously, simplifying the input requirements while expanding editing flexibility
3Manufacturing precision
If conventional pose modification techniques do not consider user surroundings, then processing is simpler, but the generated poses are inaccurate and irrelevant to the context
Solution Approach 1:
The system segments the input image analysis into multiple independent feature extraction components: user body characteristics, clothing properties, object features, and environmental context. Each segment is processed separately and then integrated to generate contextually relevant poses, improving accuracy while managing complexity through modular processing
Data Source
AI summary
A method for modifying a user pose in an input image is provided. The method includes extracting, from the input image, a plurality of features associated with at least one user and at least one object and determining one or more possible user interactions with the at least one object. Furthermore, the method includes determining a joint-object score and generating a set of user poses corresponding to the at least one object. Further, the method includes determining a containment score and determining an optimal user pose amongst the generated set of user poses. Furthermore, the method includes modifying the user pose associated with the at least one user and an object orientation associated with the at least one object in the input image.