Content Structure Matching for Automated Action Replacement
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Solution Overview
Problem
Existing methods for improving the appearance of actions in content, such as movies or videos, are labor-intensive and time-consuming, requiring manual retouching or the use of stunt doubles, which are inefficient and computationally expensive.
Innovation Solution
A system that leverages a library of deconstructed content structures to find expert-level action mappings using fingerprint matching and neural networks, allowing for the substitution of inexpert actions with expert actions, thereby improving the appearance of actions without manual retouching or stunt doubles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual retouching or stunt doubles are used to improve action appearance, then the quality of action performance is improved, but the time consumption and computational cost increase significantly
Solution Approach 1:
The system pre-processes and deconstructs expert action videos into structured content representations (mappings, attributes, fingerprints) before they are needed. This preliminary structuring allows for rapid matching and substitution during actual content improvement tasks, eliminating the need for time-consuming manual retouching or stunt double coordination.
Solution Approach 2:
The system creates digital copies of expert actions by deconstructing them into structured content representations. These copied action patterns can then be substituted into target content, replacing inexpert performances without requiring physical stunt doubles or manual frame-by-frame retouching.
2Manufacturing precision
If manual retouching or stunt doubles are used to improve action appearance, then the quality of action performance is improved, but the computational expense increases significantly
Solution Approach 1:
The system performs computationally intensive deconstruction and fingerprinting operations in advance, organizing expert actions into efficiently queryable structures. This preliminary computational work reduces the processing burden during actual substitution tasks, as the system only needs to perform fingerprint matching and structure assembly rather than full analysis.
Solution Approach 2:
The system replaces traditional mechanical approaches (manual retouching, physical stunt double work) with automated computational processes. Neural networks and fingerprint matching algorithms automatically identify and substitute expert actions, eliminating the need for labor-intensive manual operations while reducing overall computational expense through efficient algorithm design.
3Productivity
If a library of deconstructed content structures is used to automatically substitute expert actions, then the productivity is improved, but the system complexity increases
Solution Approach 1:
The system segments the complex task of action improvement into distinct modular components: video deconstruction, attribute extraction, fingerprint generation, fingerprint matching, and content reconstruction. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable despite its computational complexity.
Solution Approach 2:
The system introduces structured content representations (mappings, attributes, fingerprints) as intermediary data structures between the input video and the output improved content. These intermediaries serve as standardized interfaces that facilitate automatic processing while isolating the complexity of the transformation logic from the overall system architecture.
Data Source
AI summary
Systems and methods are disclosed herein for generating content based on matching mappings by implementing deconstruction and reconstruction techniques. The system may retrieve a first content structure that includes a first object with a first mapping that includes a first list of attribute values. The system may then search content structures for a matching content structure having a second object with a second list of attributes and a second mapping including second attribute values corresponding to the second list of attributes. Upon finding a match, the system may generate a new content structure having the first object from the first content structure with the second mapping from the matching content structure. The system may then generate for output a new content segment based on the newly generated content structure.