The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
/ Authors
Qiuyu Chen, Xin Jin, Yue Song, Xihui Liu, Shuai Yang, Tao Yang, Ziqiang Li, Jianguo Huang, Yuntao Wei, Baao Xie
and 37 more authors
N. Sebe, Wenjun Zeng, Jooyeol Yun, Davide Abati, Mohamed Omran, Jaegul Choo, A. Habibian, A. Wiggers, Masato Kobayashi, Ning Ding, Toru Tamaki, Marzieh Gheisari, A. Genovesio, Yuheng Chen, Dingkun Liu, Xinyao Yang, Xinping Xu, Baicheng Chen, Dongrui Wu, Junhao Geng, Lexiang Lv, Jianxin Lin, Hanzhe Liang, Jie Zhou, Xuanxin Chen, Jinbao Wang, Can Gao, Zhangyi Wang, Zongze Li, Bihan Wen, Yi-Xuan Gao, Xiaohan Pan, Xin Li, Zhibo Chen, Bao Peng, Zhongming Chen, Haoran Jin
/ Abstract
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop aimed to bridge the gap between the theoretical promise of Disentangled Representation Learning (DRL) and its application in realistic scenarios, moving beyond synthetic benchmarks. DRL4Real focused on evaluating DRL methods in practical applications such as controllable generation, exploring advancements in model robustness, interpretability, and generalization. The workshop accepted 9 papers covering a broad range of topics, including the integration of novel inductive biases (e.g., language), the application of diffusion models to DRL, 3D-aware disentanglement, and the expansion of DRL into specialized domains like autonomous driving and EEG analysis. This summary details the workshop's objectives, the themes of the accepted papers, and provides an overview of the methodologies proposed by the authors.
Journal: ArXiv