Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool

Suzan Ece Ada; M. Yunus Seker

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arXiv · 2111.14934

Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool

Suzan Ece Ada·M. Yunus Seker

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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.

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cs.GR cs.AI cs.CV cs.LG cs.NE

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BibTeX RIS

Suzan Ece Ada, M. Yunus Seker. 2021-11-29. Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool. https://arxiv.org/abs/2111.14934

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