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

arXiv: https://arxiv.org/abs/2111.14934 · HTML: https://mlsanity.com/paper/2111.14934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Sketchformer: Transformer-based Representation for Sketched Structure](https://mlsanity.com/paper/2002.10381/) (2020)
- [Synthesizing Programs for Images using Reinforced Adversarial Learning](https://mlsanity.com/paper/1804.01118/) (2018)
- [Teaching GANs to Sketch in Vector Format](https://mlsanity.com/paper/1904.03620/) (2019)
- [Learning to Sketch with Deep Q Networks and Demonstrated Strokes](https://mlsanity.com/paper/1810.05977/) (2018)
- [StrokeCoder: Path-Based Image Generation from Single Examples using Transformers](https://mlsanity.com/paper/2003.11958/) (2020)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning](https://mlsanity.com/paper/2506.04399/) (2025)

## Abstract (arXiv)

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.