Name
Abstract | ||
|---|---|---|
This paper presents an end-to-end pipeline using Generative Adversarial Networks (GANs) for face construction based on speech-based descriptions, and iterative editing of the generated image to arrive at a close approximation of the expected face. We propose a dialog-based interaction with the system where the user and system take turns providing descriptions and generating images respectively. A rule-based Natural Language (NL) Parser is used to extract facial attribute descriptors from textual descriptions, MSG-Style GAN (Multi-Scale Gradient Style GAN) for face generation, and Attribute GAN (AttGAN) for facial attribute manipulation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTAI50040.2020.00104 | 2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) |
Keywords | DocType | ISSN |
Generative Adversarial Network, Convolutional Neural Network, Face Construction, Image Generation, Natural Language Parser | Conference | 1082-3409 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Malaika Vijay | 1 | 0 | 0.34 |
| Meghana | 2 | 0 | 0.34 |
| Nishant Aklecha | 3 | 0 | 0.34 |
| Ramamoorthy Srinath | 4 | 0 | 2.37 |