A Study on Cognitive Effects of Canvas Size for Augmenting Drawing Skill
Abstract: In recent years, the field of generative artificial intelligence, particularly in the domain of image generation, has exerted a profound influence on society. Despite the capability of AI to produce images of high quality, the augmentation of users' drawing abilities through the provision of drawing support systems emerges as a challenging issue. In this study, we propose that a cognitive factor, specifically, the size of the canvas, may exert a considerable influence on the outcomes of imitative drawing sketches when utilizing reference images. To investigate this hypothesis, a web based drawing interface was utilized, designed specifically to evaluate the effect of the canvas size's proportionality to the reference image on the fidelity of the drawings produced. The findings from our research lend credence to the hypothesis that a drawing interface, featuring a canvas whose dimensions closely match those of the reference image, markedly improves the precision of user-generated sketches.
- A. Voynov, K. Aberman, and D. Cohen-Or, “Sketch-guided text-to-image diffusion models,” in ACM SIGGRAPH 2023 Conference Proceedings. New York, NY, USA: ACM, 2023, pp. 55:1–55:11.
- Y. Peng, C. Zhao, H. Xie, T. Fukusato, and K. Miyata, “Sketch-guided latent diffusion model for high-fidelity face image synthesis,” IEEE Access, pp. 5770–5780, 2023.
- Agency for Cultural Affairs, “[the population of artists in our country (by occupation and age)] wagakuni no geijutuka jinkou 1 (shokugyo betsu, nenrei betsu) (in japanese, title translated by the author of this article),” in Collection of Data Related to Culture and the Arts, 2015, (in Japanese).
- D. Cohen, “Look little, look often: The influence of gaze frequency on drawing accuracy,” Perception & Psychophysics, vol. 67, pp. 997–1009, 2005.
- G. Onishi, T. Kashio, S. Yorifuji, A. Kochi, and T. Syoji, “Extraction of feature quantities for drawing a good portrait using a gaze point measurement,” Transactions of Japan Society of Kansei Engineering, vol. 15, no. 4, pp. 553–561, 2016, (in Japanese).
- C. Devue and C. Barsics, “Outlining face processing skills of portrait artists: Perceptual experience with faces predicts performance,” Vision Research, vol. 127, pp. 92–103, 2016.
- C. Devue and G. Grimshaw, “Face processing skills predict faithfulness of portraits drawn by novices,” Psychonomic Bulletin & Review, vol. 25, pp. 2208–2214, 2018.
- Y. J. Lee, C. Zitnick, and M. Cohen, “Shadowdraw: Real-time user guidance for freehand drawing,” ACM Transactions on Graphics (ToG), vol. 30, no. 4, pp. 27:1–27:10, 2011.
- H. Kanayama, H. Xie, and K. Miyata, “Illustration drawing interface with image retrieval and adjustable grid guidance,” in Proceedings of Nicograph International (NicoInt). IEEE, 2023, pp. 54–61.
- Z. Huang, Y. Peng, T. Hibino, C. Zhao, H. Xie, T. Fukusato, and K. Miyata, “Dualface: Two-stage drawing guidance for freehand portrait sketching,” Computational Visual Media, vol. 8, pp. 63–77, 2022.
- Z. Huang, H. Xie, T. Fukusato, and K. Miyata, “Anifacedrawing: Anime portrait exploration during your sketching,” in ACM SIGGRAPH 2023 Conference Proceedings. New York, NY, USA: ACM, 2023, pp. 14:1–14:11.
- A. Rivers, A. Adams, and F. Durand, “Sculpting by numbers,” ACM Transactions on Graphics (ToG), vol. 31, no. 6, pp. 157:1–157:7, 2012.
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