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.
arXiv:2412.12048v2 Announce Type: replace
Abstract: Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning of image diffusion models, with over 200K models trai...
By Chenxi Liu, Towaki Takikawa, Alec Jacobson
arXiv:2602.08059v2 Announce Type: replace-cross
Abstract: Text-to-image diffusion models can reproduce specific artists visual styles at extremely low cost, raising copyright and deployment safety co...
By Tong Zhang, Ru Zhang, Jianyi Liu
arXiv:2412.00176v4 Announce Type: replace
Abstract: We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investi...
By Hui Ren, Joanna Materzynska, Rohit Gandikota, Giannis Daras, David Bau, Antonio Torralba
MAST (Mask‑Guided Attention Control for Training‑Free Regional‑Multi Style Transfer) is a framework that enables diffusion models to apply multiple reference styles to user‑specified regions of a content image without any training or optimization. It introduces logit‑level attention mass allocation, sharpness‑aware temperature scaling, and discrepancy‑aware detail injection to address mass allocation, selectivity, and detail loss problems in regional‑multi style transfer. Experiments with two to five styles show that MAST outperforms baselines in ArtFID, FID, and R‑FID, achieving high regional style fidelity, content preservation, and scalability.
By Dongkyung Kang, Jaeyeon Hwang, Junseo Park, Minji Kang, Yeryeong Lee, Beomseok Ko, Hanyoung Roh, Jeongmin Shin, Hyeryung Jang
The paper introduces Chameleon, a two‑stage training framework for cross‑domain image compositing that separates style and content representations. It first trains a ChameleonEncoder using Joint Hard Contrastive Learning to disentangle style and content, then applies Spatio‑Temporal Attention Gating within a diffusion transformer to stylize the foreground while preserving its identity. The authors also release ChameleonDataset, the first large‑scale training set for cross‑domain compositing, and demonstrate that Chameleon outperforms existing in‑domain, cross‑domain, and commercial models in both plausibility and stylistic fidelity.
By Sukhun Ko, Soo Ye Kim, Jihyong Oh
Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified and explicit definition of image style remains l...
arXiv:2608. 19719v1 Announce Type: cross Abstract: Reference-based diffusion stylization requires separating target geometry from transferable appearance.
By Jingtao Zhang, Haorui Gao, Youqing Liang, Zeming Liu
The paper introduces Motion Style Slider, a framework that enables continuous, endpoint‑supervised control of style intensity in human motion diffusion. By constructing a style direction in a learned motion‑style embedding space and conditioning diffusion generation with a scalar intensity, the method achieves smooth, monotonic style scaling without needing intermediate‑intensity ground truth. The approach is compatible with pretrained diffusion backbones, supports heterogeneous style datasets, and is evaluated on controllability, interpolation/extrapolation, content preservation, and motion realism.
By Chen-Chieh Liao, Yichen Peng, Yiyi Cai, Y\^ui Ono, Hiroki Hanaoka, Erwin Wu, Hideki Koike, Shuichi Kurabayashi
MegaStyle++ introduces a hierarchical definition of image style, ranging from overall style identity to fine‑grained visual attributes, to provide a more structured, transferable, and interpretable representation. Using this definition, the authors refined the MegaStyle annotation pipeline and released MegaStyle++‑8M, a dataset with 150K style identities, 1M fine‑grained prompts, and 8M stylized images. Analyses show that the hierarchical approach expands style diversity and semantic breadth while accurately capturing the intrinsic visual style of reference images.
By Junyao Gao, Sibo Liu, Jiaxing Li, Yanan Sun, Weidong Zhang, Cairong Zhao, Jun Zhang
arXiv:2609.38136v1 Announce Type: new
Abstract: Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects,...
By Teng Zhou, Yunhao Chen
The paper introduces Abstraction in Style (AiS), a generative framework that separates structural abstraction from visual stylization. AiS first creates an abstraction proxy that reinterprets a target image’s structure according to the abstraction logic of a given style, then renders this proxy to produce a stylized output. By learning transformations from visual exemplars without explicit geometric supervision, AiS enables a broader range of stylistic transformations, improves controllability, and allows more expressive stylization.
By Min Lu, Yuanfeng He, Anthony Chen, Jianhuang He, Pu Wang, Daniel Cohen-Or, Hui Huang