A four-stage AI illustration pipeline with a large-language-model tutor aims to scaffold novice drawing skill acquisition by exposing intermediate diffusion outputs.
A Value-Oriented Investigation of Photoshop's Generative Fill
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abstract
The creative industry is both concerned and enthusiastic about how generative AI will reshape creativity. How might these tools interact with the workflow values of creative artists? In this paper, we adopt a value-sensitive design framework to examine how generative AI, particularly Photoshop's Generative Fill (GF), helps or hinders creative professionals' values. We obtained 566 unique posts about GF from online forums for creative professionals who use Photoshop in their current work practices. We conducted reflexive thematic analysis focusing on usefulness, ease of use, and user values. Users found GF useful in doing touch-ups, expanding images, and generating composite images. GF helped users' values of productivity by making work efficient but created a value tension around creativity: it helped reduce barriers to creativity but hindered distinguishing 'human' from algorithmic art. Furthermore, GF hindered lived experiences shaping creativity and hindered the honed prideful skills of creative work.
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SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills
A four-stage AI illustration pipeline with a large-language-model tutor aims to scaffold novice drawing skill acquisition by exposing intermediate diffusion outputs.