图像生成

LaVieID: Local Autoregressive Diffusion Transformers for Identity-Preserving Video Creation

Wenhui Song, Hanhui Li, Jiehui Huang, Panwen Hu, Yuhao Cheng, Long Chen, Yiqiang Yan, Xiaodan Liang

机构 * Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区） ； Hong Kong University of Science and Technology（香港科学与技术大学） ； Mohamed bin Zayed University of Artificial Intelligence（马尔代夫 bin Zayed 人工智能大学） ； Lenovo Research（联想研究） 纠错

专题命中 个性化与一致性 ：diffusion(title,abstract)；分类 cs.CV

Comments Accepted to ACM MM 2025

LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation

Donald Shenaj, Ondrej Bohdal, Mete Ozay, Pietro Zanuttigh, Umberto Michieli

专题命中 个性化与一致性 ：image generation(title,abstract)；分类 cs.CV

Comments ICCV 2025. Project page: https://donaldssh.github.io/LoRA.rar

Learning User Preferences for Image Generation Model

Wenyi Mo, Ying Ba, Tianyu Zhang, Yalong Bai, Biye Li

专题命中 个性化与一致性 ：image generation(title)；分类 cs.CV

StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation

Shuyuan Tu, Yueming Pan, Yinming Huang, Xintong Han, Zhen Xing, Qi Dai, Chong Luo, Zuxuan Wu, Yu-Gang Jiang

机构 * Fudan University（复旦大学） ； Microsoft Research Asia（微软亚洲研究院） ； Xi’an Jiaotong University（西安交通大学） ； Hunyuan, Tencent Inc.（腾讯 Hunyuan 实验室） 纠错

专题命中 个性化与一致性 ：diffusion(abstract)；分类 cs.CV

SynMatch: Rethinking Consistency in Medical Image Segmentation with Sparse Annotations

Zhiqiang Shen, Peng Cao, Xiaoli Liu, Jinzhu Yang, Osmar R. Zaiane

专题命中 个性化与一致性 ：image synthesis(abstract)；分类 cs.CV

Recommendation with Generative Models

Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, Rene Vidal, Maheswaran Sathiamoorthy, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci

专题命中 个性化与一致性 ：image generation(abstract)

Comments This submission is a full-length book, expanding significantly on two chapters previously submitted (arXiv:2409.10993v1, arXiv:2408.10946v1). It includes additional chapters, context, analysis, and content, providing a comprehensive presentation of the subject. We have ensured it is appropriately presented as a new, distinct work. arXiv admin note: substantial text overlap with arXiv:2409.10993