The paper introduces a multimodal generative framework that fine‑tunes Stable Diffusion XL with LoRA and couples it to a LLaMA 1.5‑7B language model to produce culturally faithful Batak Ulos motifs. Four conditioning mechanisms—text, image, representation, and semantic map—are jointly used to steer the generation, and an ablation study shows that combining all four actually degrades performance compared to selective combinations. A web prototype and user studies with weavers and the public demonstrate the system’s practical value for preserving and innovating Ulos design.
By Humasak Simanjuntak, Tamara Yunika Sianipar, Bronson T. M Siallagan, Difya Laurensya Ambarita, Arlinta Barus
arXiv:2608.23302v1 Announce Type: new
Abstract: Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural mul...
By Matteo Attimonelli, Claudio Pomo, Alessandro De Bellis, Danilo Danese, Dietmar Jannach, Tommaso Di Noia
Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem where models must inter...
arXiv:2608. 14405v1 Announce Type: cross Abstract: Art style is a signature of professional digital artists that develops through repeated experimentation, reflection, and adaptation.
By Wen-Fan Wang, TsaiHsuan Lin, Chi-Lan Yang, An-Ru Cheng, Bing-Yu Chen
DetailAnywhere introduces a new fashion detail generation task that focuses on generating photorealistic close‑up views of specific garment regions, such as collars or cuffs, based on a focus marker in a reference image. The authors release FDBench, a benchmark of over 40,000 human‑verified detail pairs across 41 categories, and propose Cross‑Modal Feature Alignment Distillation (CFAD) to align multimodal diffusion branches using a DINOv3 teacher. A consistency reward model further improves alignment, and experiments show DetailAnywhere surpasses existing open‑source methods in both quantitative metrics and human evaluation.
By Zijun Li, Yimin Zhou, Jia Sun, Honglie Wang, Pengcheng Wei, Junlong Wu, Yongrui Heng, Jiyuan Wang, Huan Ouyang, Boheng Zhang, Huaiqing Wang, Dewen Fan, Qianqian Gan, Fan Yang, Tingting Gao
Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's global style. Conventional style transfer methods transfer the style of one or a few reference artworks to a content image in a One-to-One manner, making them effective for artwork-level stylization but limited in representing the broader stylistic distribution of an artist.