The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification.
One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces.
Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensics.
Biphasic facial age translation aims at predicting the appearance of the input face at any age.
Generative Adversarial Networks (GANs) with style-based generators (e. g. StyleGAN) successfully enable semantic control over image synthesis, and recent studies have also revealed that interpretable image translations could be obtained by modifying the latent code.
Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years.
Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively.
In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model.
Ranked #1 on Iris Segmentation on CASIA
Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings.
We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them.
This Estimation-Correction-Tuning process perfectly combines the advantages of the global robustness of data-driven method (FCN), outlier correction capability of model-driven method (PDM) and non-parametric optimization of RLMS.