Style Transfer Neural Network for Automated Video Frame Segmentation
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Solution Overview
Problem
Existing methods for generating stylized videos require manual processing of each frame, making it time-consuming to create a composite of multiple styles, and are not cost-effective for producing high-quality videos with low production value segments.
Innovation Solution
A computer system using a style transfer neural network to automate the process by segmenting video frames, selecting styles based on object size or image characteristics, and combining stylized segments to create final video frames, which can depict content in various styles without manual extraction and assembly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual processing of each video frame is used to generate stylized videos, then style transfer can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical processing of video frames with an automated neural network system. The neural network performs style transfer operations automatically, substituting human manual intervention with an intelligent automated system that processes frames efficiently without sacrificing style transfer quality.
Solution Approach 2:
The neural network system is designed to autonomously perform style transfer operations on video frames without requiring manual extraction and assembly. The system self-manages the entire workflow from input video to stylized output, eliminating the need for human operators to manually process each frame.
2Adaptability or versatility
If multiple styles are combined in each video frame, then creative versatility is improved, but the complexity of processing increases
Solution Approach 1:
The patent segments the video frame into multiple regions and applies different style images to different segments. This segmentation approach allows multiple styles to be combined in a single frame while managing complexity through organized regional processing rather than attempting to blend all styles uniformly across the entire frame.
Solution Approach 2:
Different segments of the video frame are assigned different style characteristics based on local requirements. Each segment receives a style that is appropriate for its specific content, allowing versatile multi-style composition while maintaining processing efficiency through localized style application rather than global complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for film-making using style transfer. One of the methods includes receiving an initial video comprising a sequence of initial video frames; receiving a selection of style images; for each initial video frame in the sequence of initial video frames, processing the initial video frame to generate a final video frame, the processing comprising: segmenting the initial video frame to generate a segmented video frame; generating a plurality of stylized video frames each according to a respective one of the style images; and generating a final video frame comprising, for each segment of the segmented video frame: determining a stylized video frame, extracting the respective segment from the determined stylized video frame, and inserting the extracted segment into the final video frame; and combining each generated final video frame in sequence to generate the final video.