# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 3 Proposed System  
The proposed encoder-decoder model is shown in the upper section of Figure 1: \"Proposal of Drawing Sequence by Generative AI.\" The system is designed as a convolutional encoder-decoder network that predicts the next step in the drawing sequence based on the current frame.  
Figure 2 shows the architecture of the proposed model. It consists of a convolutional encoder that extracts hierarchical features from the input image, followed by a convolutional decoder that reconstructs the image of the next step. Each convolutional layer in the encoder uses a \(3\times 3\) kernel with ReLU activation and batch normalization, progressively downsampling the spatial resolution. The decoder mirrors this structure, using transposed convolution layers to upsample the feature maps and restore spatial resolution.  
During training, Gaussian noise is optionally added to the input images to improve robustness against perturbations and enhance generalization. The model is trained to map the input image at time step \(t\) to the image at time step \(t+1\), and this mapping is recursively applied across the sequence. In the final step, the model is trained to reproduce the same image, ensuring consistency. This recursive prediction mechanism enables the model to generate multi-step drawing sequences from a single initial sketch.  
The model is structurally similar to a convolutional autoencoder, except for differences in the training data. While convolutional autoencoders are typically used for self-organizing image features [27], our model is designed as a generator for time-series images. While recurrent neural networks (RNNs) [28] and Transformer-based models are commonly used for sequential learning due to their ability to retain contextual information through hidden states or attention mechanisms, our model does not incorporate any such contextual layers. Instead, it relies on the structure of the training data to learn temporal progression implicitly. Although this limits its capacity to model long-term dependencies, it simplifies implementation and reduces the number of trainable parameters. As a result, the current model is not capable of handling time-series data that requires rich contextual understanding.  
Other commonly used models for image learning and generation include Variational Autoencoders (VAEs) [4] and diffusion models [29]. A Variational Autoencoder (VAE) introduces a mechanism to estimate the mean and variance in an autoencoder, generating images by sampling vectors from the learned distribution. Meanwhile, diffusion models achieve high-performance image generation through iterative noise addition and removal. These models typically require large amounts of image data for training. However, since our study involves constructing and training a custom database, we use a standard convolutional autoencoder-based model, which allows for some degree of image generation even with a limited amount of training data. As our structured database expands, we plan to further investigate the effectiveness of these models. In future work, we also plan to explore hybrid architectures, such as combining convolutional encoders with Transformer-based decoders, to better handle long-range dependencies and spatial-temporal interactions that are difficult to model using convolutional structures alone.