# Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System
## 2 Related Works
### Research on Generative AI for Drawing Support  
A deep generative model, namely AI Sketcher, has been designed to produce high-quality sketches by addressing the limitations of previous models like Sketch-RNN. It employs a CNN-based autoencoder to capture positional information at the pixel level and introduces an influence layer to guide stroke generation more precisely. This model supports multi-class sketch generation through a conditional vector, demonstrating its effectiveness on large-scale sketch datasets [8].  
Similarly, Sketch-pix2 seq has been proposed to extend the traditional sequence-to-sequence framework by incorporating convolutional and recurrent neural networks for generating diverse sketches across multiple categories [9]. It learns both structural features and temporal stroke dependencies, contributing to more coherent and realistic sketch generation.  
Generative machine learning models, including large language models, play a significant role in supporting design ideation, early prototyping, and sketching. These models assist in generating diverse design concepts and facilitate co-creative processes between humans and AI. The interaction with these models shapes user expectations and highlights the potential and limitations of AI in creative design processes [10].  
Generative AI is also making strides in architectural design through the Sketch-to-Architecture workflow. This approach uses AI models to transform simple sketches into conceptual floorplans and 3D models, enabling rapid ideation and controlled generation of architectural renderings. This innovation represents a new direction in computer-aided architectural design, showcasing the potential of generative AI in this field [11].  
A generative AI tool called DesignAID has also been developed to support the exploration of large design spaces by generating diverse ideas and images. It combines large language models with image-generation software to create a wide range of visual concepts quickly. This tool has been found to be more inspirational and useful than traditional image search methods, although the value of generating highly diverse ideas varies [12].  
Recent research has also proposed drawing support systems tailored to specific creative workflows. For example, a four-frame manga drawing support system offers frame and layout suggestions based on user content and storyline, supporting manga creators in their visual storytelling process [13]. Another study introduced an illustration drawing interface incorporating image retrieval and adjustable grid guidance, enabling amateur users to enhance their digital illustrations through structured visual prompts [14].  
Other notable works include systems that provide process-based feedback, such as recording and replaying the drawing process via digital pens for remote tutoring [15]. Remote support systems using robotic pointing and gesture overlays have also been explored to enhance drawing instruction in collaborative settings [16].  
While the focus of these studies varies, most aim to generate or support completed images or enhance instruction. In contrast, the novelty of our system lies in its ability to generate images of the drawing process itself, enabling users to view or modify intermediate stages during creation. This contributes to both the creative flexibility and educational value of drawing support systems.