Virtual Camera Camerawork Generation Using Goodness-of-Fit Indices
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Solution Overview
Problem
Existing techniques for generating camerawork for 3D models do not effectively determine camera parameter values such as viewpoint and focal length that are suitable for the content of the 3D model, limiting the aesthetic and recognizable representation of the model in moving images.
Innovation Solution
A moving image generation apparatus that captures images of a 3D model with varying camera parameters, calculates goodness-of-fit indices to assess the suitability of these images, and generates camerawork based on these indices to determine optimal camera parameter values, ensuring a suitable viewpoint and focal length for the content of the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera parameter values are determined using conventional techniques (predetermined rules, music data, or salient region detection), then camerawork can be generated, but the camera parameters (viewpoint and focal length) are not suitable for the content of the 3D model
Solution Approach 1:
The system calculates a goodness-of-fit index that provides feedback on how well the captured image represents the 3D model content. This feedback mechanism allows the system to iteratively adjust camera parameters to achieve better suitability, resolving the contradiction between obtaining precise camera parameters and adapting to different model contents.
Solution Approach 2:
The system changes camera parameters (viewpoint, focal length) and evaluates their effectiveness using goodness-of-fit indices. By systematically varying parameters and selecting those with highest suitability scores, the system achieves both precision in parameter selection and adaptability to different 3D model contents.
2Manufacturing precision
If multiple captured images are generated with different camera parameters to find suitable viewpoints, then the aesthetic quality improves, but the calculation complexity and time increase
Solution Approach 1:
The system introduces a regression model as an intermediary that automatically evaluates the aesthetic quality and content representation of captured images. This mediator calculates goodness-of-fit indices without requiring complex manual evaluation, thus improving aesthetic quality while managing system complexity through automated assessment.
Solution Approach 2:
The system replaces manual or rule-based camera parameter evaluation with an automated regression model that uses machine learning techniques. This substitution eliminates the need for complex mechanical evaluation processes and enables efficient calculation of suitability indices for multiple camera parameter configurations.
3Ease of manufacture
If salient region detection is used to determine camera parameters, then the process is simplified, but the determined parameters do not guarantee aesthetic suitability for the model
Solution Approach 1:
The system merges salient region detection with goodness-of-fit index calculation to create a comprehensive camera parameter evaluation approach. By combining the simplicity of salient region detection with the aesthetic assessment capability of goodness-of-fit indices, the system achieves both ease of implementation and high aesthetic quality in captured images.
Data Source
AI summary
A moving image generation apparatus includes: circuitry to: capture images of a three-dimensional model using different values of a camera parameter of a virtual camera to generate captured images; calculate goodness-of-fit indices using the captured images as inputs, the goodness-of-fit indices each indicating a suitability of a corresponding captured image among the captured images; and generate camerawork that traces values of the camera parameter of the virtual camera, based on the calculated goodness-of-fit indices.