ColorwAI: Generative Colorways of Textiles through GAN and Diffusion Disentanglement
<|reference_start|>ColorwAI: Generative Colorways of Textiles through GAN and Diffusion Disentanglement: Colorway creation is the task of generating textile samples in alternate color variations maintaining an underlying pattern. The individuation of a suitable color palette for a colorway is a complex creative task, responding to client and market needs, stylistic and cultural specifications, and mood. We introduce a modification of this task, the "generative colorway" creation, that includes minimal shape modifications, and propose a framework, "ColorwAI", to tackle this task using color disentanglement on StyleGAN and Diffusion. We introduce a variation of the InterfaceGAN method for supervised disentanglement, ShapleyVec. We use Shapley values to subselect a few dimensions of the detected latent direction. Moreover, we introduce a general framework to adopt common disentanglement methods on any architecture with a semantic latent space and test it on Diffusion and GANs. We interpret the color representations within the models' latent space. We find StyleGAN's W space to be the most aligned with human notions of color. Finally, we suggest that disentanglement can solicit a creative system for colorway creation, and evaluate it through expert questionnaires and creativity theory.<|reference_end|>
@article{schaerf2024colorwai:,
    title={ColorwAI: Generative Colorways of Textiles through GAN and Diffusion
  Disentanglement},
    author={Ludovica Schaerf, Andrea Alfarano, Eric Postma},
    journal={arXiv preprint arXiv:2407.11514},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11514},
    primaryClass={cs.CV}
}