Search Results for author:
Found 9 papers, 1 papers with code
High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data
In this study, for the first time, a small dataset of Rosacea with 300 full-face images is utilized to further investigate the possibility of generating synthetic data.
An Advert Creation System for 3D Product Placements
Over the past decade, the evolution of video-sharing platforms has attracted a significant amount of investments on contextual advertising.
Methodology for Building Synthetic Datasets with Virtual Humans
Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset.
Background Matting
The current state of the art alpha matting methods mainly rely on the trimap as the secondary and only guidance to estimate alpha.
Identifying Candidate Spaces for Advert Implantation
Virtual advertising is an important and promising feature in the area of online advertising.
Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN)
One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific distribution.
Face Synthesis with Landmark Points from Generative Adversarial Networks and Inverse Latent Space Mapping
There have been a tremendous amount of attempts to detect these points from facial images however, there has never been an attempt to synthesize a random face and generate its corresponding facial landmarks.
Image and Video Processing
The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack
To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application.
Total Variation-Based Dense Depth from Multi-Camera Array
Evaluation of this method based on a well-known benchmark indicates that the proposed framework performs well in terms of accuracy when compared to the top-ranked depth estimation methods and a baseline algorithm.
