Composite Image Generation via Motion Pattern Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image output apparatuses fail to effectively combine images based on detected motion and posture, limiting the creation of dynamic and contextually relevant composite images.
Innovation Solution
A composite image generating apparatus and method that determine if the movement in input image data matches pre-stored motion data, and if so, combines corresponding composite image data with the input image data to create a dynamic and contextually enhanced image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image output apparatus extracts human figure and determines posture to combine character image, then basic composite image can be generated, but the image lacks dynamic motion matching and contextual relevance
Solution Approach 1:
The system pre-stores multiple types of motion data (walking, running, jumping, etc.) and their corresponding composite image data in advance. When a live image is captured, the system simply needs to match the detected motion against these pre-stored patterns, avoiding the need for complex real-time motion synthesis and significantly reducing computational complexity while enhancing adaptability
Solution Approach 2:
The invention segments the composite image generation process into distinct components: motion detection, motion type classification, and selective composite image application. By dividing the task into these manageable segments, the system can efficiently handle different motion types independently and combine them with appropriate graphic data without requiring a complete redesign of the entire system
2Measurement precision
If motion data and composite image data are stored in association, then accurate motion-based composite images can be generated, but data storage requirements increase
Solution Approach 1:
The system uses simplified motion data representations that capture essential motion characteristics without requiring complete high-fidelity motion capture sequences. By using representative motion patterns rather than exhaustive motion recordings, the system achieves sufficient detection accuracy while minimizing data storage requirements
Solution Approach 2:
The stored motion data structures are designed to serve multiple purposes: they enable motion detection, motion classification, and serve as templates for composite image generation. This multi-functionality reduces the need for separate data sets for each purpose, thereby reducing overall storage requirements while maintaining high measurement precision
Data Source
AI summary
A plurality of items of shot image data obtained by temporally continuous shooting are analyzed. Marking data indicating that replaced graphic data is to be combined is added to image data corresponding to an actor and the resulting data is displayed. When a preset gesture (motion) is detected, marking data indicating that replaced graphic data u is to be combined is added to image data corresponding to another actor and the resulting data is displayed. After shooting, the individual items of image data to which marking data have been added are replaced with respective replaced graphic data. Replaced graphic data are created as moving images which capture the motions of the actors.