Search Results for author:
Found 5 papers, 2 papers with code
FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion
We address these constraints by proposing a novel text-conditioned editing model, called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of diverse text descriptions to guide the editing procedure.
High Resolution Face Editing with Masked GAN Latent Code Optimization
The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i. e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially--selective treatment of local image areas.
Simultaneous regression and feature learning for facial landmarking
We develop two distinct approaches around the proposed gating mechanism: i) the first uses a gated multiple ridge descent (GRID) mechanism in conjunction with established (hand-crafted) HOG features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses, ii) the second simultaneously learns multiple-descent directions as well as binary features (SMUF) that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing.
Face Hallucination Revisited: An Exploratory Study on Dataset Bias
This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle.
Face hallucination using cascaded super-resolution and identity priors
In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors.
