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Found 4 papers, 0 papers with code
Latents2Semantics: Leveraging the Latent Space of Generative Models for Localized Style Manipulation of Face Images
Consequentially, style editing of the chosen ROIs amounts to a simple combination of (a) the ROI-mask generated from the sliced structure representation and (b) the decoded image with global style changes, generated from the manipulated (using Gaussian noise) global style and unchanged structure tensor.
Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images
Both inversion of real images and determination of controllable latent directions are computationally expensive operations.
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation
Our model fuses per-pixel local information learned using two fully convolutional depth encoders with global contextual information learned by a transformer encoder at different scales.
Latents2Segments: Disentangling the Latent Space of Generative Models for Semantic Segmentation of Face Images
Our endeavour in this work is to do away with the priors and complex pre-processing operations required by SOTA multi-class face segmentation models by reframing this operation as a downstream task post infusion of disentanglement with respect to facial semantic regions of interest (ROIs) in the latent space of a Generative Autoencoder model.
