Surface-Based Neural Image Synthesis for Pose and Style Control
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Solution Overview
Problem
Existing neural image synthesis systems struggle with controllability and realism, particularly when transferring poses and styles in human images, due to the lack of effective continuous surface-level parameterizations.
Innovation Solution
The use of continuous, surface-level parameterizations, such as UV coordinate-based representations, integrated into generative models like PCA or AutoEncoders, and descriptive models like Universal Transfer methods, to modulate decoder behavior and enhance controllability and realism in image synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional neural image synthesis systems are used, then the system is simpler to implement, but the controllability and realism of pose and style transfer are poor
Solution Approach 1:
The system segments the image processing into distinct components: surface parameterization module that decomposes images into UV coordinates and depth maps, feature extraction module, and synthesis module. This segmentation allows precise control over pose and style transfer while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimensional representation by transforming 2D images into 3D surface parameterizations with UV coordinates and depth information. This dimensional transformation enables precise controllability of pose and style transfer by operating in the parameterized space rather than direct pixel manipulation.
2Reliability
If continuous surface-level parameterizations are implemented, then controllability and realism improve, but the computational complexity increases
Solution Approach 1:
The system performs preliminary surface parameterization to decompose input images into UV coordinate maps and depth maps before the actual synthesis process. This pre-processing step organizes the data in a structured format that facilitates more efficient and reliable image synthesis with improved realism.
Solution Approach 2:
The patent transforms images into parameterized representations using UV coordinates and depth values, changing the parameter space from raw pixels to geometric attributes. This parameter transformation enables more reliable and realistic synthesis by manipulating meaningful geometric and appearance parameters rather than raw pixel values.
Data Source
AI summary
Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a two-dimensional continuous surface representation of a three-dimensional object, the continuous surface comprising a plurality of landmark locations; determining a first set of soft membership functions based on a relative location of points in the two-dimensional continuous surface representation and the landmark locations; receiving a two-dimensional input image, the input image comprising an image of the object; extracting a plurality of features from the input image using a feature recognition model; generating an encoded feature representation of the extracted features using the first set of soft membership functions; generating a dense feature representation of the extracted features from the encoded representation using a second set of soft membership functions; and processing the second set of soft membership functions and dense feature representation using a neural image decoder model to generate an output image.