Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool
Bogazici University（博阿齐奇大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
