VR Volumetric Video Editing for Spatiotemporal Consistency
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Solution Overview
Problem
Existing volumetric video technologies lack the ability to dynamically modify and align elements within the video space based on user interactions, leading to inconsistencies and limitations in editing and enhancing the video experience.
Innovation Solution
A volumetric video modification program that allows users to interact with the video through a VR system, enabling modifications such as adding, removing, or repositioning objects, and defining mobility paths, while ensuring spatiotemporal consistency using generative adversarial networks and AI techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If volumetric video is generated from multi-camera feed using conventional techniques, then the video can be viewed from multiple angles, but the video cannot be dynamically modified or edited based on user interactions
Solution Approach 1:
The patent implements dynamic modification of volumetric video by allowing users to interact with and modify video elements in real-time through VR interfaces. The system transitions from static volumetric video to dynamically editable content, where users can add, remove, or reposition objects based on their interactions during playback.
Solution Approach 2:
The patent introduces an intermediary processing layer between the multi-camera feed and the final volumetric video output. This intermediary system uses AI techniques and generative adversarial networks to handle modifications while maintaining spatiotemporal consistency, effectively mediating between user inputs and video rendering.
2Manufacturing precision
If elements are added or removed in volumetric video space, then the video can be enhanced or corrected, but inconsistencies and alignment issues arise
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors and adjusts element positions and timings to maintain spatiotemporal consistency. When users modify video elements, the system provides feedback loops that automatically realign objects and correct inconsistencies, ensuring precision without requiring complex manual adjustments.
Solution Approach 2:
The patent utilizes parameter changes in the generative adversarial networks to dynamically adjust video elements. By modifying parameters such as position, timing, and spatial coordinates, the system maintains alignment precision while allowing flexible editing operations. The AI techniques automatically adjust multiple parameters simultaneously to preserve consistency.
3Reliability
If AI techniques and generative adversarial networks are used for modification, then spatiotemporal consistency is maintained, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training generative adversarial networks on volumetric video data before actual editing operations. The AI models are prepared in advance to understand spatiotemporal relationships, allowing them to quickly process modifications during playback without requiring extensive real-time computation. This preliminary preparation reduces processing time while maintaining consistency.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for volumetric video modification is provided. The embodiment may include generating a volumetric video from a multi-camera feed. The embodiment may also include presenting the generated volumetric video to a user. The embodiment may further include receiving one or more user inputs to the presented volumetric video. The embodiment may also include modifying the volumetric video based on the one or more user inputs. The embodiment may further include presenting the modified volumetric video to the user.