Abstract
This paper presents the results of computer vision experiments in the perception of an artist drawing with analog media (pen and paper), with the aim to contribute towards a human-robot co-creative drawing system. Using data gathered from user studies with artists and illustrators, two types of CNN models were designed and evaluated. Both models use multi-camera images of the drawing surface as input. One models predicts an artist’s activity (e.g. are they drawing or not?). The other model predicts the position of the pen on the canvas. Results of different combination of input sources are presented. The overall mean accuracy is 95% (std: 7%) for predicting when the artist is present and 68% (std: 15%) for predicting when the artist is drawing. The model predicts the pen’s position on the drawing canvas with a mean squared error (in normalised units) of 0.0034 (std: 0.0099). These results contribute towards the development of an autonomous robotic system which is aware of an artist at work via camera based input. In addition, this benefits the artist with a more fluid physical to digital workflow for creative content creation.
This research is supported through an EPSRC (UK) DTP Studentship “Collaborative Drawing Systems”, Grant Reference EP/N509498/1.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Raspberry PI Camera Module V2 https://www.raspberrypi.org/products/camera-module-v2/.
- 3.
Intel Depth Camera SR305 https://www.intelrealsense.com/depth-camera-sr305/.
- 4.
References
Cabannes, V., Kerdreux, T., Thiry, L., Campana, T., Ferrandes, C.: Dialog on a canvas with a machine. arXiv:1910.04386 [cs], October 2019
Chung, S.: Drawing Operations (DOUG) (2015). https://sougwen.com/project/drawing-operations
Cooney, M., Berck, P.: Designing a robot which paints with a human: visual metaphors to convey contingency and artistry. In: ICRA-X Robots Art Program at IEEE International Conference on Robotics and Automation (ICRA), Montreal QC, Canada, p. 2, May 2019
Davis, N., Hsiao, C.P., Singh, K.Y., Magerko, B.: Co-creative drawing agent with object recognition. In: Artificial Intelligence in Interactive Digital Entertainment, Burlingame, California, USA, p. 8 (2016)
Fan, J.E., Dinculescu, M., Ha, D.: Collabdraw: an environment for collaborative sketching with an artificial agent. In: Proceedings of the 2019 on Creativity and Cognition, C&C 2019, pp. 556–561. Association for Computing Machinery, San Diego, CA, USA, June 2019. https://doi.org/10.1145/3325480.3326578
Fernando, P., Weiler, J., Kuznetsov, S., Turaga, P.: Tracking, animating, and 3D printing elements of the fine arts freehand drawing process. In: Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction - TEI 2018, Stockholm, Sweden, pp. 555–561. ACM Press (2018). https://doi.org/10.1145/3173225.3173307
Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv:1704.03477 [cs, stat], May 2017
Jansen, C., Sklar, E.: Co-creative physical drawing systems. In: ICRA-X Robots Art Program at IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, p. 2, May 2019
Jansen, C., Sklar, E.: Towards a HRI system for co-creative drawing. In: ACM/IEEE International Conference on Human-Robot Interaction (HRI), Workshop on on Exploring Creative Content in Social Robotics (2020)
Jansen, C., Sklar, E.: Exploring co-creative drawing workflows. Front. Robot. AI 8, 92 (2021)
Jongejan, J., Rowley, H., Kawashima, T., Kim, J., Fox-Gieg, N.: The Quick, Draw! - A.I. Experiment (2016). https://quickdraw.withgoogle.com/
Jorge, J., Samavati, F.: Sketch-Based Interfaces and Modeling. Springer, London (2010). https://doi.org/10.1007/978-1-84882-812-4
Karimi, P., Maher, M.L., Davis, N., Grace, K.: Deep learning in a computational model for conceptual shifts in a co-creative design system. arXiv:1906.10188 [cs, stat], June 2019
Oh, C., Song, J., Choi, J., Kim, S., Lee, S., Suh, B.: I lead, you help but only with enough details: understanding user experience of co-creation with artificial intelligence. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, Montreal QC, Canada, pp. 1–13. Association for Computing Machinery, April 2018. https://doi.org/10.1145/3173574.3174223
Olsen, L., Samavati, F.F., Sousa, M.C., Jorge, J.A.: Sketch-based modeling: a survey. Comput. Graph. 33(1), 85–103 (2009). https://doi.org/10.1016/j.cag.2008.09.013
Sarvadevabhatla, R.K., Suresh, S., Babu, R.V.: Object category understanding via eye fixations on freehand sketches. IEEE Trans. Image Process. 26(5), 2508–2518 (2017). https://doi.org/10.1109/TIP.2017.2675539
Tchalenko, J., Nam, S.H., Ladanga, M., Miall, R.C.: The gaze-shift strategy in drawing. Psychol. Aesthet. Creat. Arts 8(3), 330–339 (2014). https://doi.org/10.1037/a0036132
Van Sommers, P.: Drawing and Cognition: Descriptive and Experimental Studies of Graphic Production Processes. Cambridge University Press, Cambridge (1984)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Jansen, C., Sklar, E. (2021). Predicting Artist Drawing Activity via Multi-camera Inputs for Co-creative Drawing. In: Fox, C., Gao, J., Ghalamzan Esfahani, A., Saaj, M., Hanheide, M., Parsons, S. (eds) Towards Autonomous Robotic Systems. TAROS 2021. Lecture Notes in Computer Science(), vol 13054. Springer, Cham. https://doi.org/10.1007/978-3-030-89177-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-89177-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89176-3
Online ISBN: 978-3-030-89177-0
eBook Packages: Computer ScienceComputer Science (R0)