Neural Cinematic Space-Time View Synthesis Without Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional multi-camera systems for achieving cinematic effects in photos and videos are limited to single-frame or time-snapshot object segmentation and depth-based effects, requiring precise calibration and synchronization, which restricts the creation of smooth and dynamic camera movements.
Innovation Solution
A novel space-time view synthesis technique using neural networks that interpolates between frames without requiring camera calibration or synchronization, enabling the generation of intermediate views for smoother viewing experiences by training neural networks to perform spatial and temporal interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multi-camera systems are used for cinematic effects, then object segmentation and depth-based effects can be achieved, but the system requires precise calibration and synchronization which restricts smooth and dynamic camera movements
Solution Approach 1:
The patent replaces the mechanical calibration and synchronization system with a neural network-based computational system. Instead of using precise physical calibration procedures and synchronization hardware for multi-camera systems, the invention uses neural networks to perform view synthesis and temporal interpolation, automatically learning the relationships between camera views and temporal sequences during training, thereby eliminating the need for manual calibration and complex synchronization mechanisms
Solution Approach 2:
The patent introduces neural networks as an intermediary computational layer between the raw multi-camera inputs and the final cinematic output. The neural network acts as a mediator that processes the input from multiple cameras, performs view synthesis and temporal interpolation, and generates the final smoothed cinematic effect, thereby simplifying the overall system architecture and eliminating the need for direct calibration between cameras
2Reliability
If conventional multi-camera systems are used with calibration and synchronization, then cinematic effects can be achieved, but smooth and dynamic camera movements are restricted
Solution Approach 1:
The patent implements dynamic camera movement capability through temporal interpolation using neural networks. The system can generate intermediate frames between captured frames, enabling smooth camera movements and transitions without being constrained by the fixed capture rate and positions of physical cameras. This allows the virtual camera to move freely and dynamically while maintaining cinematic effect quality
Solution Approach 2:
The patent adds the temporal dimension to the view synthesis process by using neural networks to interpolate between frames captured at different times. This temporal interpolation capability allows the system to generate smooth transitions and dynamic camera movements by synthesizing intermediate states in the time dimension, thereby increasing camera movement flexibility without sacrificing cinematic effect quality
3Productivity
If frame rate is increased for smoother viewing, then viewing experience is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary training of neural networks during an offline phase where the model learns to perform temporal interpolation and view synthesis. Once trained, the neural network can generate intermediate frames at high frame rates during real-time playback without requiring complex processing for each frame. This preliminary action separates the heavy computational work (training) from the real-time execution, enabling high frame rates with reduced processing complexity during actual use
Data Source
AI summary
A mechanism is described for facilitating cinematic space-time view synthesis in computing environments according to one embodiment. A method of embodiments, as described herein, includes capturing, by one or more cameras, multiple images at multiple positions or multiple points in times, where the multiple images represent multiple views of an object or a scene, where the one or more cameras are coupled to one or more processors of a computing device. The method further includes synthesizing, by a neural network, the multiple images into a single image including a middle image of the multiple images and representing an intermediary view of the multiple views.