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Nested Scale-Editing for Conditional Image Synthesis
- Source :
- CVPR
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- We propose an image synthesis approach that provides stratified navigation in the latent code space. With a tiny amount of partial or very low-resolution image, our approach can consistently out-perform state-of-the-art counterparts in terms of generating the closest sampled image to the ground truth. We achieve this through scale-independent editing while expanding scale-specific diversity. Scale-independence is achieved with a nested scale disentanglement loss. Scale-specific diversity is created by incorporating a progressive diversification constraint. We introduce semantic persistency across the scales by sharing common latent codes. Together they provide better control of the image synthesis process. We evaluate the effectiveness of our proposed approach through various tasks, including image outpainting, image superresolution, and cross-domain image translation.
- Subjects :
- Ground truth
Computer science
business.industry
Pattern recognition
02 engineering and technology
010501 environmental sciences
01 natural sciences
Image (mathematics)
Visualization
Constraint (information theory)
0202 electrical engineering, electronic engineering, information engineering
Image translation
020201 artificial intelligence & image processing
Artificial intelligence
Scale (map)
business
Image resolution
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Accession number :
- edsair.doi...........de91c7b6e04b5391d2991414200fddd3