This paper presents a computational framework for quantifying aesthetics of Chinese ink wash and applying them to generative models.We define differentiable metrics for the three core elements:the compositional balanc...This paper presents a computational framework for quantifying aesthetics of Chinese ink wash and applying them to generative models.We define differentiable metrics for the three core elements:the compositional balance of“Liubai”(negative space),the calligraphic quality of“Bichu”(brushstroke),and the tonal diffusion of“Moyun”(ink wash).Using these metrics,we benchmark unpaired image-to-image systems—CycleGAN,MUNIT,ChipGAN,and diffusion pipelines with controllable methods(Style LoRA,ControlNet-Tile,IP-Adapter)—on photo-to-ink transfer.Results show a trade-off:diffusion excels at“Moyun”texture fidelity,while ChipGAN with explicit aesthetic losses better preserves“Liubai”and“Bichu”structure.The study also highlights limitations of generic image-quality metrics(e.g.,FID)for artistic evaluation.We further validate implications for phygital textile design via seamless-tiling tests and small-scale physical samples.Finally,we outline a unified,material-aware scheme embedding fabric diffusion physics(Fick’s law)into a Physics-Informed GAN objective to jointly optimize aesthetic fidelity and printability.展开更多
文摘This paper presents a computational framework for quantifying aesthetics of Chinese ink wash and applying them to generative models.We define differentiable metrics for the three core elements:the compositional balance of“Liubai”(negative space),the calligraphic quality of“Bichu”(brushstroke),and the tonal diffusion of“Moyun”(ink wash).Using these metrics,we benchmark unpaired image-to-image systems—CycleGAN,MUNIT,ChipGAN,and diffusion pipelines with controllable methods(Style LoRA,ControlNet-Tile,IP-Adapter)—on photo-to-ink transfer.Results show a trade-off:diffusion excels at“Moyun”texture fidelity,while ChipGAN with explicit aesthetic losses better preserves“Liubai”and“Bichu”structure.The study also highlights limitations of generic image-quality metrics(e.g.,FID)for artistic evaluation.We further validate implications for phygital textile design via seamless-tiling tests and small-scale physical samples.Finally,we outline a unified,material-aware scheme embedding fabric diffusion physics(Fick’s law)into a Physics-Informed GAN objective to jointly optimize aesthetic fidelity and printability.
