Sources described this process being done and seen as creatively viable for animation. In-house artists or animators develop a “core set” of original concept art representative of the original character or project. These assets form the dataset used to train any foundation image or video model the studio prefers (e.g., Stable Diffusion). The resulting fine-tuned model can then be used to drive subsequent content creation, whether producing outputs that replicate the studio’s specific characters or an aesthetic style present in the art assets. Generative AI is powered by advanced algorithms and machine learning techniques.
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For others, if you are conducting a subject-based study and want to have a swath of AI personas, or if you are unsure of what AI persona you want to invoke, these datasets can be quite valuable. Indeed, any kind of large-scale testing of AI or using AI to generate lots of outputs of synthetic data can be streamlined by leveraging an AI persona dataset. That being said, I don’t want to seemingly diminish the heroic and thankful effort of those who put together these datasets. There is admittedly more elbow grease and hard work that goes into establishing a useful and usable personas dataset.
The use cases for generative range over various topics, from writing to art and marketing to healthcare. One important thing to keep in mind is that it must be used responsibly, like any other AI tool. We can make the most of generative AI by understanding its meaning, workings, and implications. “No scraped data will be part of the pipeline once that becomes available,” said Trillo.
