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The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results

Fuente: arXiv

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Hauptverfasser:	Chen, Qiuyu, Jin, Xin, Song, Yue, Liu, Xihui, Yang, Shuai, Yang, Tao, Li, Ziqiang, Huang, Jianguo, Wei, Yuntao, Xie, Ba'ao, Sebe, Nicu, Wenjun, Zeng, Yun, Jooyeol, Abati, Davide, Omran, Mohamed, Choo, Jaegul, Habibian, Amir, Wiggers, Auke, Kobayashi, Masato, Ding, Ning, Tamaki, Toru, Gheisari, Marzieh, Genovesio, Auguste, Chen, Yuheng, Liu, Dingkun, Yang, Xinyao, Xu, Xinping, Chen, Baicheng, Wu, Dongrui, Geng, Junhao, Lv, Lexiang, Lin, Jianxin, Liang, Hanzhe, Zhou, Jie, Chen, Xuanxin, Wang, Jinbao, Gao, Can, Wang, Zhangyi, Li, Zongze, Wen, Bihan, Gao, Yixin, Pan, Xiaohan, Li, Xin, Chen, Zhibo, Peng, Baorui, Chen, Zhongming, Jin, Haoran
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning Computer Vision and Pattern Recognition
Online-Zugang:	Acceder al recurso
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_version_	1866912585338585088
author	Chen, Qiuyu Jin, Xin Song, Yue Liu, Xihui Yang, Shuai Yang, Tao Li, Ziqiang Huang, Jianguo Wei, Yuntao Xie, Ba'ao Sebe, Nicu Wenjun Zeng Yun, Jooyeol Abati, Davide Omran, Mohamed Choo, Jaegul Habibian, Amir Wiggers, Auke Kobayashi, Masato Ding, Ning Tamaki, Toru Gheisari, Marzieh Genovesio, Auguste Chen, Yuheng Liu, Dingkun Yang, Xinyao Xu, Xinping Chen, Baicheng Wu, Dongrui Geng, Junhao Lv, Lexiang Lin, Jianxin Liang, Hanzhe Zhou, Jie Chen, Xuanxin Wang, Jinbao Gao, Can Wang, Zhangyi Li, Zongze Wen, Bihan Gao, Yixin Pan, Xiaohan Li, Xin Chen, Zhibo Peng, Baorui Chen, Zhongming Jin, Haoran
author_facet	Chen, Qiuyu Jin, Xin Song, Yue Liu, Xihui Yang, Shuai Yang, Tao Li, Ziqiang Huang, Jianguo Wei, Yuntao Xie, Ba'ao Sebe, Nicu Wenjun Zeng Yun, Jooyeol Abati, Davide Omran, Mohamed Choo, Jaegul Habibian, Amir Wiggers, Auke Kobayashi, Masato Ding, Ning Tamaki, Toru Gheisari, Marzieh Genovesio, Auguste Chen, Yuheng Liu, Dingkun Yang, Xinyao Xu, Xinping Chen, Baicheng Wu, Dongrui Geng, Junhao Lv, Lexiang Lin, Jianxin Liang, Hanzhe Zhou, Jie Chen, Xuanxin Wang, Jinbao Gao, Can Wang, Zhangyi Li, Zongze Wen, Bihan Gao, Yixin Pan, Xiaohan Li, Xin Chen, Zhibo Peng, Baorui Chen, Zhongming Jin, Haoran
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_10463
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results Chen, Qiuyu Jin, Xin Song, Yue Liu, Xihui Yang, Shuai Yang, Tao Li, Ziqiang Huang, Jianguo Wei, Yuntao Xie, Ba'ao Sebe, Nicu Wenjun Zeng Yun, Jooyeol Abati, Davide Omran, Mohamed Choo, Jaegul Habibian, Amir Wiggers, Auke Kobayashi, Masato Ding, Ning Tamaki, Toru Gheisari, Marzieh Genovesio, Auguste Chen, Yuheng Liu, Dingkun Yang, Xinyao Xu, Xinping Chen, Baicheng Wu, Dongrui Geng, Junhao Lv, Lexiang Lin, Jianxin Liang, Hanzhe Zhou, Jie Chen, Xuanxin Wang, Jinbao Gao, Can Wang, Zhangyi Li, Zongze Wen, Bihan Gao, Yixin Pan, Xiaohan Li, Xin Chen, Zhibo Peng, Baorui Chen, Zhongming Jin, Haoran Machine Learning Computer Vision and Pattern Recognition 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.
title	The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
topic	Machine Learning Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2509.10463