Landscape Painter: Mimicking Human Like Art Using Generative Adversarial Networks (2024)

Abstract: Generating paintings using AI has been an intriguing area of research and has posed significant challenges in recent years. Landscape painting is a type of man-made ecological art form which contributes to preserving the ecological integrity of the environment we live in. Generative AI based Painting constitutes a form of visual expression encompassing various elements like drawings, arrangement, and conceptualization. Existing generative models do not replicate the painting process followed by a human painter. A human artist creates artwork in various stages such as: Sketching, Outlining and Colouring. Current generative models frequently restrict the range and diversity of styles by depending solely on carefully selected datasets such as WikiArt and VanGogh. The proposed work intends to utilize scraping techniques to collect a wide range of comprehensive and diverse landscape paintings. The primary objective of this research is to apply various generative AI models to generate artwork that replicate a human painting process and encompasses various artistic themes and styles instead of relying on a particular one. Performance of our work has shown that the landscape painting generation into distinct sketch and color phases have proven to be effective, fun and realistic.

## introduction painting constitutes a form of visual expression that encompasses various elements like drawings, arrangement, and conceptualization. it serves as a means for artists to portray their viewpoints and understandings of the world surrounding them, encompassing genres such as still life, religious motifs, landscapes, surrealism, symbolism, historical occurrences, emotions, and politics etc.,. some of the examples of different types traditional and folk art forms are tanjore painting, madhubani painting, warli, pattachitra and kalamkari etc., @xcite . landscape painting encompasses various subgenres, such as rural and urban landscapes, seascapes, marine landscapes, ancient ruins, and forests, among others @xcite . current machine art models do not replicate the process by which a human painter creates an artwork; rather, they rely primarily on pre-existing datasets such as wikiart or vangogh, which restricts the multiplicity and scope of the paintings that are generated (choudhury, 2020). @xcite describes a method of generating kandinsky art using generation of content and style images. this approach is limited to kandinsky art and is not applicable for other kinds of art forms. @xcite estimated generative models by means of an adversarial process. this approach fails to perform well in case of high resolution images and complex images. some models are trained on small-scale traditional painting element, they cannot provide sufficient diversity for untrained categories. our proposed method attempts to resolve few of the above mentioned limitations such as mimicking the human artistic style and not restricting the model to generate landscape painting from the dataset given during the training phase. it splits the image generation process into two phase: first being, sketcher and other being colorize. since, the images are scarped from various domains and model is trained accordingly the stylegan2 based model outperforms from cnn based dcgan. ## about the dataset a vast array of extensive and varied data was meticulously gathered from a multitude of digital platforms, encompassing prominent sources such as freepik (landscape painting freepik), pinterest (landscape oil paintings by craig houston; landscape paintings by mark vandervinne), and google images (landscape painting). following the extraction process, a rigorous filtration procedure was implemented to eradicate any redundant entries, subpar resolution images, as well as any content that exhibited the presence of watermarks. to augment the dataset and improve its diversity, all images were horizontally mirrored by rotating them 180 degrees to the right. the dataset comprises a total of 5396 images. ## design of our proposed landscape painting generation the proposed work is novel from the existing literature where we generate human like artistic painting and does not rely on artistic features of particular dataset, instead it can generate diverse artistic features from different domains. ## conclusions and future directions the partitioning of the landscape painting generation into distinct sketch and color phases has proven to be effective, resulting in realistic and interesting outcomes. further investigation should concentrate on refining techniques that take into account lines and figures in the coloring process to enhance the overall quality of the generated artworks.