Global and Spatial Autoencoder for Image Generation
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Solution Overview
Problem
Conventional digital image editing systems are inaccurate, inefficient, and inflexible, struggling to generate specific digital images due to reliance on arbitrary sampling and extensive training data requirements, which limits their ability to adapt to on-the-fly image manipulation.
Innovation Solution
The use of a global and spatial autoencoder that splits a digital image's latent code into spatial and global components, allowing for hybridization and manipulation of these codes to generate specific images, enabling accurate and efficient digital image manipulation by swapping and blending styles and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep generative models (GANs) are used to generate digital images from random samples, then the ability to produce realistic images is improved, but the accuracy of generating specific user-selected images deteriorates
Solution Approach 1:
The latent code is segmented into two independent components: a global code representing overall image properties and a spatial code representing geometric layout. This segmentation allows the model to accurately generate specific images by combining the appropriate global code with the spatial code from the user-selected image, resolving the contradiction between producing realistic images and accurately generating specific images.
2Measurement precision
If conditional generative models are used to synthesize output conditional on input digital image, then the accuracy of generating specific images is improved, but the computational efficiency and flexibility deteriorate due to extensive training data and retraining requirements
Solution Approach 1:
A single universal autoencoder model is trained once on diverse image data to learn the mapping between images and their (global code, spatial code) representations. This pre-trained model can then efficiently generate any specific image by simply swapping the global code, eliminating the need for retraining for different applications and dramatically improving computational efficiency and flexibility.
Solution Approach 2:
The model performs preliminary action by pre-training the autoencoder on a comprehensive dataset to learn general image representations and the relationship between global and spatial codes. This one-time preliminary training enables efficient and accurate generation of specific images without requiring subsequent retraining, thus resolving the contradiction between accuracy and computational efficiency.
3Adaptability or versatility
If pretrained unconditional GAN models are retrofitted with semantic editing operations in latent space, then the flexibility of image manipulation is improved, but the computational cost and time consumption increase
Solution Approach 1:
By segmenting the latent code into global and spatial components, the model achieves flexible image manipulation through simple code swapping operations rather than complex retrofitted operations. The spatial code can be directly manipulated to achieve geometric edits, while the global code handles stylistic changes, providing flexibility with significantly reduced computational cost.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified digital image from extracted spatial and global codes. For example, the disclosed systems can utilize a global and spatial autoencoder to extract spatial codes and global codes from digital images. The disclosed systems can further utilize the global and spatial autoencoder to generate a modified digital image by combining extracted spatial and global codes in various ways for various applications such as style swapping, style blending, and attribute editing.