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Found 3 papers, 3 papers with code
QC-StyleGAN -- Quality Controllable Image Generation and Manipulation
The network can synthesize various image degradation and restore the sharp image via a quality control code.
TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation
Our study outlines several issues in the current evaluation pipeline: (i) for image quality assessment, a commonly used metric, e. g., Inception Score (IS), is often either miscalibrated for the single-object case or misused for the multi-object case; (ii) for text relevance and object accuracy assessment, there is an overfitting phenomenon in the existing R-precision (RP) and Semantic Object Accuracy (SOA) metrics, respectively; (iii) for multi-object case, many vital factors for evaluation, e. g., object fidelity, positional alignment, counting alignment, are largely dismissed; (iv) the ranking of the methods based on current metrics is highly inconsistent with real images.
HyperInverter: Improving StyleGAN Inversion via Hypernetwork
In the first phase, we train an encoder to map the input image to StyleGAN2 $\mathcal{W}$-space, which was proven to have excellent editability but lower reconstruction quality.
