Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool
Graphics
2021-12-23 v2 Artificial Intelligence
Computer Vision and Pattern Recognition
Machine Learning
Neural and Evolutionary Computing
Abstract
Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adversarial loss on CNMP to increase sketch smoothness and consistency. Through the experiments, we show that our model can be trained with few unlabeled samples, can construct distributions automatically in the latent space, and produces better results than the base model in terms of shape consistency and smoothness.
Cite
@article{arxiv.2111.14934,
title = {Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool},
author = {Suzan Ece Ada and M. Yunus Seker},
journal= {arXiv preprint arXiv:2111.14934},
year = {2021}
}
Comments
9 pages, 10 figures