Blended Video Sequence Face Morphing Transition
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Solution Overview
Problem
Conventional methods for blending still images and videos in slideshows often result in abrupt transitions, disrupting the flow of presentations, and require manual user intervention, which is time-consuming and inefficient, especially when faces are present in both still photos and videos.
Innovation Solution
A method that automatically blends still images and videos by detecting and aligning facial features, using a morphing algorithm to create seamless transitions between faces and backgrounds, eliminating the need for manual frame selection and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional transition methods (fade-in, abrupt cuts) are used between still images and videos, then the implementation is simple and fast, but the transition quality is poor and disrupts the presentation flow
Solution Approach 1:
The patent segments the still image into multiple regions (face region and non-face regions) and processes each region differently during transition. The face region undergoes morphing transformation while non-face regions use simpler blending, allowing high-quality face transitions without applying complex processing to the entire image, thus resolving the contradiction between transition quality and processing complexity.
Solution Approach 2:
The patent applies different transition qualities and methods to different regions of the image. High-quality morphing is applied specifically to the face region where visual continuity is most important, while simpler techniques are used for background regions. This local differentiation achieves high overall transition quality without requiring complex processing everywhere, resolving the technical contradiction.
2Manufacturing precision
If manual frame selection and alignment is performed for face transitions, then the transition accuracy is high, but the time consumption and user effort increase significantly
Solution Approach 1:
The patent implements automatic face detection, localization, and alignment systems that perform tasks previously requiring manual user intervention. The system automatically identifies faces in still images and corresponding frames in video sequences, aligns them using feature point matching, and executes transitions without user input. This automation maintains high alignment accuracy while eliminating the time-consuming manual operations, resolving the contradiction between precision and time consumption.
Solution Approach 2:
The patent replaces manual mechanical operations (hand-adjusting frames, manually aligning faces) with automated computational methods including face detection algorithms, feature point extraction, and automatic alignment calculations. This substitution of manual mechanical processes with automated computational systems achieves the same alignment accuracy without the time penalty of manual intervention, resolving the technical contradiction.
3Stability of the object's composition
If comprehensive face detection and frame selection algorithms are implemented, then the transition smoothness is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent divides the image processing task into segments: face detection, face region extraction, feature point identification, and transition execution. By segmenting the comprehensive algorithm into modular components, the system achieves smooth transitions through careful face-region-focused processing without requiring complex algorithms to process the entire image, resolving the contradiction between smoothness and algorithmic complexity.
Solution Approach 2:
The patent applies complex face-matching algorithms only to the face region rather than the entire image, and applies morphing transformations only where needed for smooth transitions. This partial application of complex algorithms to specific regions achieves the necessary transition smoothness without the computational overhead of applying comprehensive processing to all image areas, resolving the technical contradiction.
Data Source
AI summary
A method for producing a blended video sequence that combines a still image and a video image sequence comprising: designating a first face in the still image, designating a second face in the video image sequence; detecting a series of video frames in the video image sequence containing the second face; identifying a video frame in the detected series of video frames suitable for transitioning from the first face into the second face; using a data processor to automatically produce a transition image sequence where the first face transitions into the second face, and a first background transitions into a second background; and producing the blended video sequence by concatenating the transition image sequence, and a plurality of video frames from the video image sequence starting from the identified video frame.