Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System

With rapid advances in image-generative AI, interest has grown in applying these technologies across diverse domains. While current models excel at producing high-quality final images, they rarely model the intermediate stages of the drawing process. This study proposes a drawing support system that leverages generative AI to sequentially generate and modify images during the drawing process. To train the system, we constructed a custom dataset of time-series drawing images, which are typically unavailable in existing AI models. We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. To address error accumulation during recursive generation, we introduced a noise-augmented training method, incorporating noisy images into the dataset. Experimental results show that standard training suffers from image degradation over time, while the noise-augmented approach significantly improves stability and quality throughout the sequence. Averaged across all generated frames, the noise-augmented training achieved a PSNR of 20.48, SSIM of 0.81, and LPIPS of 0.13. As a benchmark, we compared this approach to PredRNN, a representative temporal model, which achieved a PSNR of 24.05, SSIM of 0.88, and LPIPS of 0.08. These results demonstrate the effectiveness of noise-augmented learning while also highlighting potential performance gains using temporal architectures. PredRNN also required more computation, with 6.41 M parameters and a 0.188 s inference time per sequence, compared to 5.20 M and 0.005 s for our model. Furthermore, in a sample-level analysis of 10 final images, the proposed model outperformed PredRNN in three cases across all metrics, suggesting its robustness in certain sequences despite architectural simplicity. Future directions include using more advanced models such as Variational Autoencoders and diffusion models, and enhancing consistency in long-term sequences. Our work confirms the feasibility of generating interactive drawing sequences using AI and sets the foundation for more robust and creative drawing support tools.