Neural Image Generation with Optical Flow Reconstruction
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Solution Overview
Problem
Generating high-resolution image and video content at higher frame rates is resource-intensive and challenging for devices with limited capacity, often constrained by quality and timing requirements.
Innovation Solution
A renderer generates lower-resolution images, which are then upscaled using a neural network-based image reconstruction module that includes a warp network and a reconstruction network, trained together with a combined loss function to optimize performance, allowing for real-time high-resolution image generation without relying on motion vectors from the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution image and video content is generated using traditional rendering approaches, then image quality and display resolution are improved, but processing resources and computational capacity requirements increase significantly
Solution Approach 1:
The system segments the image processing task into two distinct stages: (1) a renderer that generates low-resolution images efficiently, and (2) a neural network-based image reconstruction module that upscales the low-resolution images to high-resolution. This segmentation allows each component to optimize for its specific function, reducing overall computational resource requirements while maintaining high image quality.
Solution Approach 2:
The patent introduces an intermediary neural network module that acts as a mediator between the low-resolution renderer output and the final high-resolution display. This intermediary reconstruction module uses trained neural networks to intelligently upscale images, achieving high-resolution output without requiring the entire rendering pipeline to operate at high resolution, thus reducing processing resource consumption.
2Productivity
If high-resolution content is generated at high frame rates, then video quality and smoothness are improved, but processing capacity and timing requirements become difficult to meet
Solution Approach 1:
The system segments the frame generation process into (1) rapid low-resolution rendering that can achieve high frame rates with minimal computational complexity, and (2) a neural network upscaling step that operates on the low-resolution frames. This segmentation enables the system to meet high frame rate requirements while keeping processing complexity manageable, as the complex high-resolution generation is performed only on the simplified low-resolution inputs.
Solution Approach 2:
The neural network model is pre-trained to perform image reconstruction and upscaling, allowing the system to leverage learned patterns and relationships from training data during real-time operation. This preliminary training enables the system to handle complex upscaling tasks efficiently during actual video generation, reducing the computational burden during high-frame-rate rendering.
3Speed
If traditional rendering is used to meet target frame rates, then timing requirements are satisfied, but image quality is constrained by resolution limitations
Solution Approach 1:
The system segments the image generation pipeline to allow the rendering stage to operate at high frame rates with low resolution, while the neural network reconstruction stage enhances the image quality. This segmentation decouples the frame rate generation (handled by the fast renderer) from the quality enhancement (handled by the neural network), enabling both high frame rates and high image quality to coexist.
Solution Approach 2:
The system dynamically changes the resolution parameter during the pipeline: the renderer operates at a lower resolution to achieve high frame rates, and then the neural network upscales the images to the desired high resolution. This parameter change allows the system to optimize for frame rate during rendering and then optimize for quality during reconstruction, satisfying both timing requirements and image quality expectations.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, at least a first optical flow network (OFN) and at least a first reconstruction network (RN) can be used to generate one or more images based, at least in part, upon the OFN and the RN using a shared loss function.