Supporting Reference Imagery for Digital Drawing
Josh Holinaty, Alec Jacobson, Fanny Chevalier
University of Toronto
Best Paper Award at the Sketching for Human Expressivity Workshop at IEEE/CVF International Conference on Computer Vision, 2021
Figure 1. Artists adopt a variety of strategies when using reference imagery during the creation of drawings. We design and deploy a technology probe to log how artists organize and access their reference imagery, while also exploring design solutions to address limitations described in our formative artist interviews. Our probe identifies common strategies artists adopt relative to why they are using reference, and artists responded positively to the just-in-time presentation of reference it provides.
There is little understanding in the challenges artists face when using reference imagery while creating drawings digitally. How can this part of the creative process be better supported during the act of drawing? We conduct formative interviews with artists and reveal many adopt ad hoc strategies when integrating reference into their workflows. Interview results inform the design of a novel sketching interface in form of a technology probe to capture how artists use and access reference imagery, while also addressing opportunities to better support the use of reference, such as just-in-time presentation of imagery, automatic transparency to assist tracing, and features to mitigate design fixation. To capture how reference is used, we tasked artists to complete a series of digital drawings using our probe, with each task having particular reference needs. Artists were quick to adopt and appreciate the novel solutions provided by our probe, and we identified common strategies that can be exploited to support reference imagery in future creative tools.
Video Trailer
    author = {Holinaty, Josh and Jacobson, Alec and Chevalier, Fanny},
    title = {Supporting Reference Imagery for Digital Drawing},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month = {October},
    year = {2021},
    pages = {2434-2442}