# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 1 Introduction  
With the emergence of image-generative AI such as Stable Diffusion and Midjourney, research on the integration of generative AI across various fields has been actively conducted. Examples of such fields range from entertainment and media [1] to gaming and virtual worlds [2]. These AIs are provided in a highly user-friendly manner for both personal and commercial use, allowing even non-experts to use them without understanding the internal structure of the model. They are capable of generating high-quality images, significantly contributing to the efficiency of various tasks. However, most existing generative AI systems focus on producing a single final image. The intermediate stages of creation--the process of drawing--are often neglected, despite their value in educational and creative contexts. The drawing process contains essential information for understanding structure, style, and technique, especially for learners and creators. A system that can generate and visualize these stages can greatly enhance user understanding and engagement. In our research, we focus on creating a drawing support system using generative AI. The system under development is designed to implement the following two features:  
1. The generative AI creates a drawing sequence, including the drawing process, allowing users to modify any stage of the process to refine the final image. 2. The user inputs images of the drawing process into the generative AI, which then generates a balanced image based on the provided input. In this paper, we specifically deal with creating a basic model of the first feature. A schematic diagram of the model is shown in Figure 1.  
To build such a system, it is necessary to develop a generative AI capable of sequentially generating a series of drawing images. While there are prior studies that use RNNs or vector-based models to simulate stroke-by-stroke sketching [3,4], they are often limited to symbolic or vector inputs and do not directly operate on pixel-based sketch images. Figure 1: Schematic diagram of the model. Our approach focuses on raster images of the drawing process, which are more commonly used in practice and do not require specialized annotation. To sequentially generate time-series images of the drawing process, it is necessary to train the model using images of the drawing process. However, while current generative AI models are trained on a vast amount of completed images available on the web, images of the drawing process are generally not publicly shared. Therefore, collecting training data becomes a major challenge for training this system. In order to address this challenge, we built our own training database. Specifically, we focused on images of human figures, creating various character images and continuously adding images of their drawing processes to the database. By using the constructed database, we built and trained a generative AI model to verify the effectiveness of our approach. This drawing support system has potential applications in a wide range of fields, such as interactive learning platforms for art education, AI-assisted illustration tools, and creative assistance for users with limited drawing skills or motor impairments. Rather than simply generating final outputs, our model aims to assist users through each stage of the creative process. The main contributions of this study are summarized as follows:  
* We propose a novel drawing sequence-generation system based on a convolutional encoder-decoder architecture, which enables step-by-step image synthesis from a single sketch input. * We introduce a noise-augmented training strategy that significantly improves sequence stability by reducing accumulated prediction error, a critical challenge in recursive image generation. * We conduct comprehensive comparisons with a state-of-the-art recurrent baseline (PredRNN), providing both qualitative and quantitative evaluations using the PSNR, SSIM, LPIPS, FID, inference time, and parameter count.