# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 6 Conclusions and Future Work  
In this study, we proposed a drawing support system using generative AI that generates not only the final image but also the entire drawing sequence. To achieve this, we developed a CNN-based encoder-decoder model trained on a custom-built dataset of time-series drawing images. Our experimental results confirmed that introducing noise-augmented images in the training process significantly improves image sequence generation by reducing accumulated errors. To further validate our approach, we compared our model with PredRNN, a state-of-the-art recurrent model for spatiotemporal prediction. While PredRNN outperformed our method in terms of average PSNR, SSIM, and LPIPS across the generated sequence, our noise-augmented encoder-decoder showed competitive performance, particularly in early frames. This suggests that, even without temporal memory mechanisms, simple convolutional architectures can still perform reasonably well under certain conditions. Moreover, a case-based evaluation of the final generated frames revealed that our proposed model outperformed PredRNN in 3 out of 10 samples. Despite its lower average scores, this result suggests that the proposed method can be more effective for certain types of drawing sequences or structural patterns. Such cases highlight the potential of noise-augmented training to offer robustness and localized performance gains, even in comparison with more complex temporal models. In addition, our model offers advantages in training efficiency and architectural simplicity, making it suitable for applications where computational cost and model size are critical factors. These trade-offs highlight potential use cases in lightweight or interactive systems, especially where real-time responsiveness is prioritized over long-term consistency. The quantitative comparison confirmed this advantage: our model had fewer parameters (5.20 M vs. 6.41 M in PredRNN) and generated a sequence 37 times faster (0.005 s vs. 0.188 s), making it more viable for deployment on resource-limited devices or time-sensitive applications. In future work, we plan to investigate the following directions:  
1. Expand the training dataset by incorporating a larger variety of character drawings and refining the annotation process. 2. Integrate advanced generative models, such as Variational Autoencoders (VAEs) and diffusion models, to improve image quality and long-term consistency. 3. Improve the current encoder-decoder architecture by incorporating skip connections (e.g., U-Net structure) to enable more effective learning on higher-resolution images, and evaluate the model's applicability to finer-detail drawing tasks. 4. Explore the use of temporal architectures, such as ConvLSTM or hybrid CNN-RNN models, to capture contextual dependencies across frames. 5. Evaluate inference speed and parameter efficiency in detail to quantify the trade-offs between model simplicity and performance. 6. Develop a user-interactive interface that allows real-time modifications to the generated drawing process, making the system more practical for creative applications. By addressing these challenges, we aim to further improve the capabilities of AI-assisted drawing tools, bridging the gap between generative AI and human creativity. Conceptualization, H.M. and S.N. ; methodology, H.M.; software, H.M.; validation, H.M., A.N. and S.N. ; formal analysis, S.N. ; investigation, H.M.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, S.N. and A.N. ; visualization, H.M.; supervision, S.N. ; project administration, S.N. All authors have read and agreed to the published version of the manuscript. This research was supported by JSPS KAKENHI under grant number JP23K11277. Not applicable. The dataset used in this study consists of hand-created drawing process images and is not publicly available due to privacy and copyright considerations, as the hand-drawn data constitutes the personal intellectual property of the creators.