Panorama Generation with Homography Estimation for Camera-Free Inputs
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Solution Overview
Problem
Existing text-to-image diffusion models struggle with generating 360-degree panoramas from camera-free inputs due to disrupted generalization capabilities and the requirement of known intrinsic and rotation matrices, limiting their application to predefined images.
Innovation Solution
A machine learning model comprising a first sub-model for estimating unknown camera parameters through a homography transformation and a second sub-model for generating multi-view consistent panoramas using correspondence-aware attention, integrated in a fully differentiable manner, to extend panorama generation to camera-free inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-to-image diffusion models are used to generate 360-degree panoramas from camera-free inputs, then panorama generation capability is extended, but generalization capabilities are disrupted and errors in homography estimation occur
Solution Approach 1:
The model is divided into two sub-models: a homography estimation sub-model and a panorama generation sub-model. This segmentation allows each sub-model to specialize in its specific task, with the homography estimation sub-model focusing on accurate parameter estimation and the panorama generation sub-model focusing on high-quality image synthesis, thereby resolving the contradiction between extended capability and reliability
Solution Approach 2:
The homography estimation sub-model performs preliminary estimation of camera parameters before the panorama generation process. This preliminary action provides accurate geometric transformations as input to the generation model, ensuring that the subsequent panorama generation maintains high generalization capability while extending to camera-free inputs
2Manufacturing precision
If known intrinsic and rotation matrices are required for panorama generation, then multi-view consistency is achieved, but application is limited to predefined images
Solution Approach 1:
The homography estimation sub-model enables the system to self-determine the required intrinsic and rotation matrices from the input image itself, without requiring external predefined parameters. This self-service mechanism allows the model to automatically adapt to any input image while maintaining multi-view consistency, thereby extending application scope without sacrificing precision
Solution Approach 2:
The model transforms the requirement from fixed predefined parameters to dynamically estimated parameters. By changing from static known matrices to dynamically estimated homography parameters, the system maintains multi-view consistency through accurate estimation while becoming applicable to any arbitrary input image
Data Source
AI summary
The present disclosure describes techniques for generating a panorama based on an input image using a machine learning model. The input image with unknown camera parameters is received by the machine learning model. A first sub-model of the machine learning model estimates a homography matrix from the input image to a predefined canonical view. The homography matrix comprises three degrees of freedom and indicates pixel-level correspondences between the input image and the predefined canonical view. A second sub-model of the machine learning model generates a plurality of perspective views based on the homography matrix and a text description of an environment associated with the input image. the second sub-model of the machine learning model is configured to generate new content for extended areas while preserving existing image content. The panorama is generated based on the plurality of perspective views.