360-Degree Panorama Generation Using Guided Co-Modulation
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Solution Overview
Problem
Existing image synthesis methods struggle to generate high-resolution, realistic 360-degree panoramas from narrow field of view images, often resulting in artifacts and discontinuities at the edges.
Innovation Solution
A guided co-modulation generator network is used to generate full 360-degree panorama images from input narrow field of view images, employing a pretrained guide model to control the panorama generation process and ensure semantic matching with the input image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If image synthesis methods are used to generate 360-degree panoramas from narrow field of view images, then the field of view can be extrapolated, but artifacts and discontinuities appear at the edges
Solution Approach 1:
The system performs preliminary actions by estimating camera parameters and generating a panoramic projection of the input image before feeding it to the generator model. This preliminary processing prepares the image data in a format that facilitates smoother transitions and reduces artifacts during the panorama generation process
Solution Approach 2:
The generator model receives feedback through the discriminator network that evaluates the generated panorama's realism and consistency. This feedback loop enables the model to iteratively improve the output, reducing artifacts and discontinuities at the edges by learning from the discriminator's assessments
2Reliability
If learning-based methods are used to generate panoramas, then realistic results are achieved, but the generation process is computationally intensive
Solution Approach 1:
The system segments the panorama generation task into distinct components: camera parameter estimation, panoramic projection generation, and the main generator model. This segmentation allows each component to be optimized independently and enables progressive processing, reducing the overall computational burden while maintaining realism
Solution Approach 2:
By performing camera parameter estimation and panoramic projection generation as preliminary actions before the main generation process, the system prepares the input data in advance. This reduces the computational load during the critical generation phase and enables more efficient use of computational resources
3Measurement precision
If a guided co-modulation generator network is used with a pretrained guide model, then semantic matching with the input image is improved, but the device complexity increases
Solution Approach 1:
The pretrained guide model serves multiple functions: it provides semantic guidance for the generator, enables class-conditioned generation, and maintains consistency with the input image. This multi-functionality reduces the need for separate specialized components, thereby managing complexity while improving semantic accuracy
Solution Approach 2:
The guide model acts as an intermediary between the input image and the generator model. It processes the input image to extract semantic information and transforms it into guidance signals that the generator can use. This intermediary layer simplifies the overall architecture by centralizing the semantic processing function
Data Source
AI summary
Embodiments are disclosed for generating 360-degree panoramas from input narrow field of view images. A method of generating 360-degree panoramas may include obtaining an input image and guide, generating a panoramic projection of the input image, and generating, by a panorama generator, a 360-degree panorama based on the panoramic projection and the guide, wherein the panorama generator is a guided co-modulation generator network trained to generate a 360-degree panorama from the input image based on the guide.