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Sketches are abstract representations of visual perception and visuospatialconstruction. In this work, we proposed a new framework, Generative AdversarialNetworks with Conditional Neural Movement Primitives (GAN-CNMP), thatincorporates a novel adversarial loss on CNMP to increase sketch smoothness andconsistency. Through the experiments, we show that our model can be trainedwith few unlabeled samples, can construct distributions automatically in thelatent space, and produces better results than the base model in terms of shapeconsistency and smoothness.

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