Copying style, Extracting value: Illustrators' Perception of AI Style Transfer and its Impact on Creative Labor
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917906339594240 |
|---|---|
| author | Porquet, Julien Wang, Sitong Chilton, Lydia B. |
| author_facet | Porquet, Julien Wang, Sitong Chilton, Lydia B. |
| contents | Generative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives. We provided four illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model's successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies aesthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style. They also deemed the others' copies more successful. Understanding the results of style transfer as "boundary objects," we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others. We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17410 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Copying style, Extracting value: Illustrators' Perception of AI Style Transfer and its Impact on Creative Labor Porquet, Julien Wang, Sitong Chilton, Lydia B. Human-Computer Interaction Generative text-to-image models are disrupting the lives of creative professionals. Specifically, illustrators are threatened by models that claim to extract and reproduce their style. Yet, research on style transfer has rarely focused on their perspectives. We provided four illustrators with a model fine-tuned to their style and conducted semi-structured interviews about the model's successes, limitations, and potential uses. Evaluating their output, artists reported that style transfer successfully copies aesthetic fragments but is limited by content-style disentanglement and lacks the crucial emergent quality of their style. They also deemed the others' copies more successful. Understanding the results of style transfer as "boundary objects," we analyze how they can simultaneously be considered unsuccessful by artists and poised to replace their work by others. We connect our findings to critical HCI frameworks, demonstrating that style transfer, rather than merely a Creativity Support Tool, should also be understood as a supply chain optimization one. |
| title | Copying style, Extracting value: Illustrators' Perception of AI Style Transfer and its Impact on Creative Labor |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.17410 |