Unified Neural Network for Frame Interpolation and Extrapolation
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Solution Overview
Problem
Current neural frame generation techniques for 3D rendered applications face challenges in achieving a balance between image quality and performance, requiring multiple iterations to optimize interpolation and extrapolation methods, and often necessitate separate networks for interpolation and extrapolation tasks.
Innovation Solution
A unified neural network is developed that can perform both frame interpolation and extrapolation by merging interpolation and extrapolation networks into a single architecture, allowing for adaptable operation based on latency requirements without the need for retraining, using sparse optical flow data for training and upscaling to dense data for frame generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If separate neural networks are developed for interpolation and extrapolation tasks, then image quality can be optimized for each specific task, but device complexity and development time increase significantly
Solution Approach 1:
The patent combines separate interpolation and extrapolation neural networks into a single unified network architecture. The unified network receives multiple input frames and generates output frames for both interpolation and extrapolation tasks, reducing the number of separate models from two to one while maintaining task-specific performance through a shared feature extraction backbone and task-specific output heads.
Solution Approach 2:
The unified neural network is designed to perform multiple functions - both frame interpolation and frame extrapolation - within a single model. The network architecture includes a shared feature extraction module that processes input frames, followed by task-specific branches that handle different prediction objectives, allowing one model to serve multiple purposes without requiring separate specialized networks for each task.
2Manufacturing precision
If high resolution optical flow data is used for frame generation, then image quality improves, but computational performance and processing speed decrease
Solution Approach 1:
The patent segments the optical flow processing into two distinct resolutions: low resolution for the main frame generation task and high resolution for detailed refinement. The network first generates frames using computationally efficient low resolution optical flow, then applies a refinement module that incorporates high resolution optical flow details only where needed, reducing overall computational burden while preserving image quality.
Solution Approach 2:
Instead of processing the entire optical flow field at high resolution, the patent applies high resolution processing only to critical regions or selectively to specific areas that require detailed accuracy. The majority of the frame generation uses low resolution optical flow, with high resolution details applied partially to enhance quality only where necessary, thereby balancing performance and image quality.
3Productivity
If sparse optical flow data is used for training, then training efficiency improves, but the quality of generated frames may deteriorate compared to using dense optical flow data
Solution Approach 1:
The patent introduces an intermediate upsampling stage that converts sparse optical flow data into dense optical flow representations during training. The network learns to upsample sparse flow fields to dense ones, using the upsampled dense flow as an intermediate representation that bridges the gap between efficient sparse input and high quality frame generation requirements.
Data Source
AI summary
Described herein are techniques to enhance the user experience for 3D rendered applications via neural frame generation using upsampled optical flow data. In one embodiment, a neural network is trained using both sparse optical flow data and dense optical flow data to enable neural frame generation to be performed by a deployed neural network using only sparse optical flow data. The sparse optical flow data can be upsampled to dense optical flow data by the trained neural network. The neural network can then use the upsampled dense optical flow data to perform frame generation.