Improved Generation of Drawing Sequences Using Variational and Skip-Connected Deep Networks for a Drawing Support System

This study presents a deep generative model designed to predict intermediate stages in the drawing process of character illustrations. To enhance generalization and robustness, the model integrates a variational bottleneck based on the Variational Autoencoder (VAE) and employs Gaussian noise augmentation during training. We also investigate the effect of U-Net-style skip connections, which allow for the direct propagation of low-level features, on autoregressive sequence generation. Comparative experiments with baseline models demonstrate that the proposed VAE with noise augmentation outperforms both CNN- and RNN-based baselines in long-term stability and visual fidelity. While skip connections improve local detail retention, they also introduce instability in extended sequences, suggesting a trade-off between spatial precision and temporal coherence. The findings highlight the advantages of probabilistic modeling and data augmentation for sequential image generation and provide practical insights for designing intelligent drawing support systems.